diff --git a/cmake/GtsamCythonWrap.cmake b/cmake/GtsamCythonWrap.cmake index 6f378f77d..73e2b63e0 100644 --- a/cmake/GtsamCythonWrap.cmake +++ b/cmake/GtsamCythonWrap.cmake @@ -52,7 +52,7 @@ function(pyx_to_cpp target pyx_file generated_cpp include_dirs) add_custom_command( OUTPUT ${generated_cpp} COMMAND - ${CYTHON_EXECUTABLE} -X boundscheck=False -a -v --fast-fail --cplus ${includes_for_cython} ${pyx_file} -o ${generated_cpp} + ${CYTHON_EXECUTABLE} -X boundscheck=False -v --fast-fail --cplus ${includes_for_cython} ${pyx_file} -o ${generated_cpp} VERBATIM) add_custom_target(${target} ALL DEPENDS ${generated_cpp}) endfunction() @@ -95,7 +95,7 @@ function(cythonize target pyx_file output_lib_we output_dir include_dirs libs in # Late dependency injection, to make sure this gets called whenever the interface header is updated # See: https://stackoverflow.com/questions/40032593/cmake-does-not-rebuild-dependent-after-prerequisite-changes - add_custom_command(OUTPUT ${generated_cpp} DEPENDS ${interface_header} APPEND) + add_custom_command(OUTPUT ${generated_cpp} DEPENDS ${interface_header} ${pyx_file} APPEND) if (NOT "${dependencies}" STREQUAL "") add_dependencies(${target}_pyx2cpp "${dependencies}") endif() diff --git a/cython/gtsam/examples/ImuFactorExample2.py b/cython/gtsam/examples/ImuFactorExample2.py new file mode 100644 index 000000000..10bd3de11 --- /dev/null +++ b/cython/gtsam/examples/ImuFactorExample2.py @@ -0,0 +1,176 @@ +""" +iSAM2 example with ImuFactor. +Author: Robert Truax (C++), Frank Dellaert (Python) +""" +# pylint: disable=invalid-name, E1101 + +from __future__ import print_function + +import math + +import matplotlib.pyplot as plt +import numpy as np +from mpl_toolkits.mplot3d import Axes3D # pylint: disable=W0611 + +import gtsam +import gtsam.utils.plot as gtsam_plot + + +def X(key): + """Create symbol for pose key.""" + return gtsam.symbol(ord('x'), key) + + +def V(key): + """Create symbol for velocity key.""" + return gtsam.symbol(ord('v'), key) + + +def vector3(x, y, z): + """Create 3d double numpy array.""" + return np.array([x, y, z], dtype=np.float) + + +def ISAM2_plot(values, fignum=0): + """Plot poses.""" + fig = plt.figure(fignum) + axes = fig.gca(projection='3d') + plt.cla() + + i = 0 + min_bounds = 0, 0, 0 + max_bounds = 0, 0, 0 + while values.exists(X(i)): + pose_i = values.atPose3(X(i)) + gtsam_plot.plot_pose3(fignum, pose_i, 10) + position = pose_i.translation().vector() + min_bounds = [min(v1, v2) for (v1, v2) in zip(position, min_bounds)] + max_bounds = [max(v1, v2) for (v1, v2) in zip(position, max_bounds)] + # max_bounds = min(pose_i.x(), max_bounds[0]), 0, 0 + i += 1 + + # draw + axes.set_xlim3d(min_bounds[0]-1, max_bounds[0]+1) + axes.set_ylim3d(min_bounds[1]-1, max_bounds[1]+1) + axes.set_zlim3d(min_bounds[2]-1, max_bounds[2]+1) + plt.pause(1) + + +# IMU preintegration parameters +# Default Params for a Z-up navigation frame, such as ENU: gravity points along negative Z-axis +g = 9.81 +n_gravity = vector3(0, 0, -g) +PARAMS = gtsam.PreintegrationParams.MakeSharedU(g) +I = np.eye(3) +PARAMS.setAccelerometerCovariance(I * 0.1) +PARAMS.setGyroscopeCovariance(I * 0.1) +PARAMS.setIntegrationCovariance(I * 0.1) +PARAMS.setUse2ndOrderCoriolis(False) +PARAMS.setOmegaCoriolis(vector3(0, 0, 0)) + +BIAS_COVARIANCE = gtsam.noiseModel_Isotropic.Variance(6, 0.1) +DELTA = gtsam.Pose3(gtsam.Rot3.Rodrigues(0, 0, 0), + gtsam.Point3(0.05, -0.10, 0.20)) + + +def IMU_example(): + """Run iSAM 2 example with IMU factor.""" + + # Start with a camera on x-axis looking at origin + radius = 30 + up = gtsam.Point3(0, 0, 1) + target = gtsam.Point3(0, 0, 0) + position = gtsam.Point3(radius, 0, 0) + camera = gtsam.SimpleCamera.Lookat(position, target, up, gtsam.Cal3_S2()) + pose_0 = camera.pose() + + # Create the set of ground-truth landmarks and poses + angular_velocity = math.radians(180) # rad/sec + delta_t = 1.0/18 # makes for 10 degrees per step + + angular_velocity_vector = vector3(0, -angular_velocity, 0) + linear_velocity_vector = vector3(radius * angular_velocity, 0, 0) + scenario = gtsam.ConstantTwistScenario( + angular_velocity_vector, linear_velocity_vector, pose_0) + + # Create a factor graph + newgraph = gtsam.NonlinearFactorGraph() + + # Create (incremental) ISAM2 solver + isam = gtsam.ISAM2() + + # Create the initial estimate to the solution + # Intentionally initialize the variables off from the ground truth + initialEstimate = gtsam.Values() + + # Add a prior on pose x0. This indirectly specifies where the origin is. + # 30cm std on x,y,z 0.1 rad on roll,pitch,yaw + noise = gtsam.noiseModel_Diagonal.Sigmas( + np.array([0.3, 0.3, 0.3, 0.1, 0.1, 0.1])) + newgraph.push_back(gtsam.PriorFactorPose3(X(0), pose_0, noise)) + + # Add imu priors + biasKey = gtsam.symbol(ord('b'), 0) + biasnoise = gtsam.noiseModel_Isotropic.Sigma(6, 0.1) + biasprior = gtsam.PriorFactorConstantBias(biasKey, gtsam.imuBias_ConstantBias(), + biasnoise) + newgraph.push_back(biasprior) + initialEstimate.insert(biasKey, gtsam.imuBias_ConstantBias()) + velnoise = gtsam.noiseModel_Isotropic.Sigma(3, 0.1) + + # Calculate with correct initial velocity + n_velocity = vector3(0, angular_velocity * radius, 0) + velprior = gtsam.PriorFactorVector(V(0), n_velocity, velnoise) + newgraph.push_back(velprior) + initialEstimate.insert(V(0), n_velocity) + + accum = gtsam.PreintegratedImuMeasurements(PARAMS) + + # Simulate poses and imu measurements, adding them to the factor graph + for i in range(80): + t = i * delta_t # simulation time + if i == 0: # First time add two poses + pose_1 = scenario.pose(delta_t) + initialEstimate.insert(X(0), pose_0.compose(DELTA)) + initialEstimate.insert(X(1), pose_1.compose(DELTA)) + elif i >= 2: # Add more poses as necessary + pose_i = scenario.pose(t) + initialEstimate.insert(X(i), pose_i.compose(DELTA)) + + if i > 0: + # Add Bias variables periodically + if i % 5 == 0: + biasKey += 1 + factor = gtsam.BetweenFactorConstantBias( + biasKey - 1, biasKey, gtsam.imuBias_ConstantBias(), BIAS_COVARIANCE) + newgraph.add(factor) + initialEstimate.insert(biasKey, gtsam.imuBias_ConstantBias()) + + # Predict acceleration and gyro measurements in (actual) body frame + nRb = scenario.rotation(t).matrix() + bRn = np.transpose(nRb) + measuredAcc = scenario.acceleration_b(t) - np.dot(bRn, n_gravity) + measuredOmega = scenario.omega_b(t) + accum.integrateMeasurement(measuredAcc, measuredOmega, delta_t) + + # Add Imu Factor + imufac = gtsam.ImuFactor( + X(i - 1), V(i - 1), X(i), V(i), biasKey, accum) + newgraph.add(imufac) + + # insert new velocity, which is wrong + initialEstimate.insert(V(i), n_velocity) + accum.resetIntegration() + + # Incremental solution + isam.update(newgraph, initialEstimate) + result = isam.calculateEstimate() + ISAM2_plot(result) + + # reset + newgraph = gtsam.NonlinearFactorGraph() + initialEstimate.clear() + + +if __name__ == '__main__': + IMU_example() diff --git a/cython/gtsam/examples/OdometryExample.py b/cython/gtsam/examples/OdometryExample.py new file mode 100644 index 000000000..5d2190d56 --- /dev/null +++ b/cython/gtsam/examples/OdometryExample.py @@ -0,0 +1,52 @@ +""" +GTSAM Copyright 2010-2018, Georgia Tech Research Corporation, +Atlanta, Georgia 30332-0415 +All Rights Reserved +Authors: Frank Dellaert, et al. (see THANKS for the full author list) + +See LICENSE for the license information + +Simple robot motion example, with prior and two odometry measurements +Author: Frank Dellaert +""" +# pylint: disable=invalid-name, E1101 + +from __future__ import print_function + +import numpy as np + +import gtsam + +# Create noise models +ODOMETRY_NOISE = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.2, 0.2, 0.1])) +PRIOR_NOISE = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.3, 0.3, 0.1])) + +# Create an empty nonlinear factor graph +graph = gtsam.NonlinearFactorGraph() + +# Add a prior on the first pose, setting it to the origin +# A prior factor consists of a mean and a noise model (covariance matrix) +priorMean = gtsam.Pose2(0.0, 0.0, 0.0) # prior at origin +graph.add(gtsam.PriorFactorPose2(1, priorMean, PRIOR_NOISE)) + +# Add odometry factors +odometry = gtsam.Pose2(2.0, 0.0, 0.0) +# For simplicity, we will use the same noise model for each odometry factor +# Create odometry (Between) factors between consecutive poses +graph.add(gtsam.BetweenFactorPose2(1, 2, odometry, ODOMETRY_NOISE)) +graph.add(gtsam.BetweenFactorPose2(2, 3, odometry, ODOMETRY_NOISE)) +print("\nFactor Graph:\n{}".format(graph)) + +# Create the data structure to hold the initialEstimate estimate to the solution +# For illustrative purposes, these have been deliberately set to incorrect values +initial = gtsam.Values() +initial.insert(1, gtsam.Pose2(0.5, 0.0, 0.2)) +initial.insert(2, gtsam.Pose2(2.3, 0.1, -0.2)) +initial.insert(3, gtsam.Pose2(4.1, 0.1, 0.1)) +print("\nInitial Estimate:\n{}".format(initial)) + +# optimize using Levenberg-Marquardt optimization +params = gtsam.LevenbergMarquardtParams() +optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initial, params) +result = optimizer.optimize() +print("\nFinal Result:\n{}".format(result)) diff --git a/cython/gtsam/examples/PlanarSLAMExample.py b/cython/gtsam/examples/PlanarSLAMExample.py new file mode 100644 index 000000000..fb038d043 --- /dev/null +++ b/cython/gtsam/examples/PlanarSLAMExample.py @@ -0,0 +1,80 @@ +""" +GTSAM Copyright 2010-2018, Georgia Tech Research Corporation, +Atlanta, Georgia 30332-0415 +All Rights Reserved +Authors: Frank Dellaert, et al. (see THANKS for the full author list) + +See LICENSE for the license information + +Simple robotics example using odometry measurements and bearing-range (laser) measurements +Author: Alex Cunningham (C++), Kevin Deng & Frank Dellaert (Python) +""" +# pylint: disable=invalid-name, E1101 + +from __future__ import print_function + +import numpy as np + +import gtsam + +# Create noise models +PRIOR_NOISE = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.3, 0.3, 0.1])) +ODOMETRY_NOISE = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.2, 0.2, 0.1])) +MEASUREMENT_NOISE = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.1, 0.2])) + +# Create an empty nonlinear factor graph +graph = gtsam.NonlinearFactorGraph() + +# Create the keys corresponding to unknown variables in the factor graph +X1 = gtsam.symbol(ord('x'), 1) +X2 = gtsam.symbol(ord('x'), 2) +X3 = gtsam.symbol(ord('x'), 3) +L1 = gtsam.symbol(ord('l'), 4) +L2 = gtsam.symbol(ord('l'), 5) + +# Add a prior on pose X1 at the origin. A prior factor consists of a mean and a noise model +graph.add(gtsam.PriorFactorPose2(X1, gtsam.Pose2(0.0, 0.0, 0.0), PRIOR_NOISE)) + +# Add odometry factors between X1,X2 and X2,X3, respectively +graph.add(gtsam.BetweenFactorPose2( + X1, X2, gtsam.Pose2(2.0, 0.0, 0.0), ODOMETRY_NOISE)) +graph.add(gtsam.BetweenFactorPose2( + X2, X3, gtsam.Pose2(2.0, 0.0, 0.0), ODOMETRY_NOISE)) + +# Add Range-Bearing measurements to two different landmarks L1 and L2 +graph.add(gtsam.BearingRangeFactor2D( + X1, L1, gtsam.Rot2.fromDegrees(45), np.sqrt(4.0+4.0), MEASUREMENT_NOISE)) +graph.add(gtsam.BearingRangeFactor2D( + X2, L1, gtsam.Rot2.fromDegrees(90), 2.0, MEASUREMENT_NOISE)) +graph.add(gtsam.BearingRangeFactor2D( + X3, L2, gtsam.Rot2.fromDegrees(90), 2.0, MEASUREMENT_NOISE)) + +# Print graph +print("Factor Graph:\n{}".format(graph)) + +# Create (deliberately inaccurate) initial estimate +initial_estimate = gtsam.Values() +initial_estimate.insert(X1, gtsam.Pose2(-0.25, 0.20, 0.15)) +initial_estimate.insert(X2, gtsam.Pose2(2.30, 0.10, -0.20)) +initial_estimate.insert(X3, gtsam.Pose2(4.10, 0.10, 0.10)) +initial_estimate.insert(L1, gtsam.Point2(1.80, 2.10)) +initial_estimate.insert(L2, gtsam.Point2(4.10, 1.80)) + +# Print +print("Initial Estimate:\n{}".format(initial_estimate)) + +# Optimize using Levenberg-Marquardt optimization. The optimizer +# accepts an optional set of configuration parameters, controlling +# things like convergence criteria, the type of linear system solver +# to use, and the amount of information displayed during optimization. +# Here we will use the default set of parameters. See the +# documentation for the full set of parameters. +params = gtsam.LevenbergMarquardtParams() +optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initial_estimate, params) +result = optimizer.optimize() +print("\nFinal Result:\n{}".format(result)) + +# Calculate and print marginal covariances for all variables +marginals = gtsam.Marginals(graph, result) +for (key, str) in [(X1, "X1"), (X2, "X2"), (X3, "X3"), (L1, "L1"), (L2, "L2")]: + print("{} covariance:\n{}\n".format(str, marginals.marginalCovariance(key))) diff --git a/cython/gtsam/examples/Pose2SLAMExample.py b/cython/gtsam/examples/Pose2SLAMExample.py new file mode 100644 index 000000000..b15b90864 --- /dev/null +++ b/cython/gtsam/examples/Pose2SLAMExample.py @@ -0,0 +1,87 @@ +""" +GTSAM Copyright 2010-2018, Georgia Tech Research Corporation, +Atlanta, Georgia 30332-0415 +All Rights Reserved +Authors: Frank Dellaert, et al. (see THANKS for the full author list) + +See LICENSE for the license information + +Simple robotics example using odometry measurements and bearing-range (laser) measurements +Author: Alex Cunningham (C++), Kevin Deng & Frank Dellaert (Python) +""" +# pylint: disable=invalid-name, E1101 + +from __future__ import print_function + +import math + +import numpy as np + +import gtsam + + +def vector3(x, y, z): + """Create 3d double numpy array.""" + return np.array([x, y, z], dtype=np.float) + + +# Create noise models +PRIOR_NOISE = gtsam.noiseModel_Diagonal.Sigmas(vector3(0.3, 0.3, 0.1)) +ODOMETRY_NOISE = gtsam.noiseModel_Diagonal.Sigmas(vector3(0.2, 0.2, 0.1)) + +# 1. Create a factor graph container and add factors to it +graph = gtsam.NonlinearFactorGraph() + +# 2a. Add a prior on the first pose, setting it to the origin +# A prior factor consists of a mean and a noise ODOMETRY_NOISE (covariance matrix) +graph.add(gtsam.PriorFactorPose2(1, gtsam.Pose2(0, 0, 0), PRIOR_NOISE)) + +# 2b. Add odometry factors +# Create odometry (Between) factors between consecutive poses +graph.add(gtsam.BetweenFactorPose2(1, 2, gtsam.Pose2(2, 0, 0), ODOMETRY_NOISE)) +graph.add(gtsam.BetweenFactorPose2( + 2, 3, gtsam.Pose2(2, 0, math.pi / 2), ODOMETRY_NOISE)) +graph.add(gtsam.BetweenFactorPose2( + 3, 4, gtsam.Pose2(2, 0, math.pi / 2), ODOMETRY_NOISE)) +graph.add(gtsam.BetweenFactorPose2( + 4, 5, gtsam.Pose2(2, 0, math.pi / 2), ODOMETRY_NOISE)) + +# 2c. Add the loop closure constraint +# This factor encodes the fact that we have returned to the same pose. In real +# systems, these constraints may be identified in many ways, such as appearance-based +# techniques with camera images. We will use another Between Factor to enforce this constraint: +graph.add(gtsam.BetweenFactorPose2( + 5, 2, gtsam.Pose2(2, 0, math.pi / 2), ODOMETRY_NOISE)) +print("\nFactor Graph:\n{}".format(graph)) # print + +# 3. Create the data structure to hold the initial_estimate estimate to the +# solution. For illustrative purposes, these have been deliberately set to incorrect values +initial_estimate = gtsam.Values() +initial_estimate.insert(1, gtsam.Pose2(0.5, 0.0, 0.2)) +initial_estimate.insert(2, gtsam.Pose2(2.3, 0.1, -0.2)) +initial_estimate.insert(3, gtsam.Pose2(4.1, 0.1, math.pi / 2)) +initial_estimate.insert(4, gtsam.Pose2(4.0, 2.0, math.pi)) +initial_estimate.insert(5, gtsam.Pose2(2.1, 2.1, -math.pi / 2)) +print("\nInitial Estimate:\n{}".format(initial_estimate)) # print + +# 4. Optimize the initial values using a Gauss-Newton nonlinear optimizer +# The optimizer accepts an optional set of configuration parameters, +# controlling things like convergence criteria, the type of linear +# system solver to use, and the amount of information displayed during +# optimization. We will set a few parameters as a demonstration. +parameters = gtsam.GaussNewtonParams() + +# Stop iterating once the change in error between steps is less than this value +parameters.setRelativeErrorTol(1e-5) +# Do not perform more than N iteration steps +parameters.setMaxIterations(100) +# Create the optimizer ... +optimizer = gtsam.GaussNewtonOptimizer(graph, initial_estimate, parameters) +# ... and optimize +result = optimizer.optimize() +print("Final Result:\n{}".format(result)) + +# 5. Calculate and print marginal covariances for all variables +marginals = gtsam.Marginals(graph, result) +for i in range(1, 6): + print("X{} covariance:\n{}\n".format(i, marginals.marginalCovariance(i))) diff --git a/cython/gtsam/examples/README.md b/cython/gtsam/examples/README.md new file mode 100644 index 000000000..067929a20 --- /dev/null +++ b/cython/gtsam/examples/README.md @@ -0,0 +1,4 @@ +These examples are almost identical to the old handwritten python wrapper +examples. However, there are just some slight name changes, for example +noiseModel.Diagonal becomes noiseModel_Diagonal etc... +Also, annoyingly, instead of gtsam.Symbol('b',0) we now need to say gtsam.symbol(ord('b'), 0)) diff --git a/cython/gtsam/examples/SFMdata.py b/cython/gtsam/examples/SFMdata.py new file mode 100644 index 000000000..1d5c499fa --- /dev/null +++ b/cython/gtsam/examples/SFMdata.py @@ -0,0 +1,38 @@ +""" +A structure-from-motion example with landmarks + - The landmarks form a 10 meter cube + - The robot rotates around the landmarks, always facing towards the cube +""" +# pylint: disable=invalid-name, E1101 + +import numpy as np + +import gtsam + + +def createPoints(): + # Create the set of ground-truth landmarks + points = [gtsam.Point3(10.0, 10.0, 10.0), + gtsam.Point3(-10.0, 10.0, 10.0), + gtsam.Point3(-10.0, -10.0, 10.0), + gtsam.Point3(10.0, -10.0, 10.0), + gtsam.Point3(10.0, 10.0, -10.0), + gtsam.Point3(-10.0, 10.0, -10.0), + gtsam.Point3(-10.0, -10.0, -10.0), + gtsam.Point3(10.0, -10.0, -10.0)] + return points + + +def createPoses(K): + # Create the set of ground-truth poses + radius = 30.0 + angles = np.linspace(0, 2*np.pi, 8, endpoint=False) + up = gtsam.Point3(0, 0, 1) + target = gtsam.Point3(0, 0, 0) + poses = [] + for theta in angles: + position = gtsam.Point3(radius*np.cos(theta), + radius*np.sin(theta), 0.0) + camera = gtsam.PinholeCameraCal3_S2.Lookat(position, target, up, K) + poses.append(camera.pose()) + return poses diff --git a/cython/gtsam/examples/VisualISAM2Example.py b/cython/gtsam/examples/VisualISAM2Example.py new file mode 100644 index 000000000..e2caee6d4 --- /dev/null +++ b/cython/gtsam/examples/VisualISAM2Example.py @@ -0,0 +1,163 @@ +""" +GTSAM Copyright 2010-2018, Georgia Tech Research Corporation, +Atlanta, Georgia 30332-0415 +All Rights Reserved +Authors: Frank Dellaert, et al. (see THANKS for the full author list) + +See LICENSE for the license information + +An example of running visual SLAM using iSAM2. +Author: Duy-Nguyen Ta (C++), Frank Dellaert (Python) +""" +# pylint: disable=invalid-name, E1101 + +from __future__ import print_function + +import matplotlib.pyplot as plt +import numpy as np +from mpl_toolkits.mplot3d import Axes3D # pylint: disable=W0611 + +import gtsam +import gtsam.utils.plot as gtsam_plot +from gtsam.examples import SFMdata + + +def X(i): + """Create key for pose i.""" + return int(gtsam.symbol(ord('x'), i)) + + +def L(j): + """Create key for landmark j.""" + return int(gtsam.symbol(ord('l'), j)) + + +def visual_ISAM2_plot(result): + """ + VisualISAMPlot plots current state of ISAM2 object + Author: Ellon Paiva + Based on MATLAB version by: Duy Nguyen Ta and Frank Dellaert + """ + + # Declare an id for the figure + fignum = 0 + + fig = plt.figure(fignum) + axes = fig.gca(projection='3d') + plt.cla() + + # Plot points + # Can't use data because current frame might not see all points + # marginals = Marginals(isam.getFactorsUnsafe(), isam.calculateEstimate()) + # gtsam.plot_3d_points(result, [], marginals) + gtsam_plot.plot_3d_points(fignum, result, 'rx') + + # Plot cameras + i = 0 + while result.exists(X(i)): + pose_i = result.atPose3(X(i)) + gtsam_plot.plot_pose3(fignum, pose_i, 10) + i += 1 + + # draw + axes.set_xlim3d(-40, 40) + axes.set_ylim3d(-40, 40) + axes.set_zlim3d(-40, 40) + plt.pause(1) + + +def visual_ISAM2_example(): + plt.ion() + + # Define the camera calibration parameters + K = gtsam.Cal3_S2(50.0, 50.0, 0.0, 50.0, 50.0) + + # Define the camera observation noise model + measurement_noise = gtsam.noiseModel_Isotropic.Sigma( + 2, 1.0) # one pixel in u and v + + # Create the set of ground-truth landmarks + points = SFMdata.createPoints() + + # Create the set of ground-truth poses + poses = SFMdata.createPoses(K) + + # Create an iSAM2 object. Unlike iSAM1, which performs periodic batch steps + # to maintain proper linearization and efficient variable ordering, iSAM2 + # performs partial relinearization/reordering at each step. A parameter + # structure is available that allows the user to set various properties, such + # as the relinearization threshold and type of linear solver. For this + # example, we we set the relinearization threshold small so the iSAM2 result + # will approach the batch result. + parameters = gtsam.ISAM2Params() + parameters.setRelinearizeThreshold(0.01) + parameters.setRelinearizeSkip(1) + isam = gtsam.ISAM2(parameters) + + # Create a Factor Graph and Values to hold the new data + graph = gtsam.NonlinearFactorGraph() + initial_estimate = gtsam.Values() + + # Loop over the different poses, adding the observations to iSAM incrementally + for i, pose in enumerate(poses): + + # Add factors for each landmark observation + for j, point in enumerate(points): + camera = gtsam.PinholeCameraCal3_S2(pose, K) + measurement = camera.project(point) + graph.push_back(gtsam.GenericProjectionFactorCal3_S2( + measurement, measurement_noise, X(i), L(j), K)) + + # Add an initial guess for the current pose + # Intentionally initialize the variables off from the ground truth + initial_estimate.insert(X(i), pose.compose(gtsam.Pose3( + gtsam.Rot3.Rodrigues(-0.1, 0.2, 0.25), gtsam.Point3(0.05, -0.10, 0.20)))) + + # If this is the first iteration, add a prior on the first pose to set the + # coordinate frame and a prior on the first landmark to set the scale. + # Also, as iSAM solves incrementally, we must wait until each is observed + # at least twice before adding it to iSAM. + if i == 0: + # Add a prior on pose x0 + pose_noise = gtsam.noiseModel_Diagonal.Sigmas(np.array( + [0.3, 0.3, 0.3, 0.1, 0.1, 0.1])) # 30cm std on x,y,z 0.1 rad on roll,pitch,yaw + graph.push_back(gtsam.PriorFactorPose3(X(0), poses[0], pose_noise)) + + # Add a prior on landmark l0 + point_noise = gtsam.noiseModel_Isotropic.Sigma(3, 0.1) + graph.push_back(gtsam.PriorFactorPoint3( + L(0), points[0], point_noise)) # add directly to graph + + # Add initial guesses to all observed landmarks + # Intentionally initialize the variables off from the ground truth + for j, point in enumerate(points): + initial_estimate.insert(L(j), gtsam.Point3( + point.x()-0.25, point.y()+0.20, point.z()+0.15)) + else: + # Update iSAM with the new factors + isam.update(graph, initial_estimate) + # Each call to iSAM2 update(*) performs one iteration of the iterative nonlinear solver. + # If accuracy is desired at the expense of time, update(*) can be called additional + # times to perform multiple optimizer iterations every step. + isam.update() + current_estimate = isam.calculateEstimate() + print("****************************************************") + print("Frame", i, ":") + for j in range(i + 1): + print(X(j), ":", current_estimate.atPose3(X(j))) + + for j in range(len(points)): + print(L(j), ":", current_estimate.atPoint3(L(j))) + + visual_ISAM2_plot(current_estimate) + + # Clear the factor graph and values for the next iteration + graph.resize(0) + initial_estimate.clear() + + plt.ioff() + plt.show() + + +if __name__ == '__main__': + visual_ISAM2_example() diff --git a/cython/gtsam/examples/__init__.py b/cython/gtsam/examples/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/cython/gtsam/tests/test_Scenario.py b/cython/gtsam/tests/test_Scenario.py new file mode 100644 index 000000000..4cca1400b --- /dev/null +++ b/cython/gtsam/tests/test_Scenario.py @@ -0,0 +1,36 @@ +import math +import unittest +import numpy as np + +import gtsam + + +class TestScenario(unittest.TestCase): + def setUp(self): + pass + + def test_loop(self): + # Forward velocity 2m/s + # Pitch up with angular velocity 6 degree/sec (negative in FLU) + v = 2 + w = math.radians(6) + W = np.array([0, -w, 0]) + V = np.array([v, 0, 0]) + scenario = gtsam.ConstantTwistScenario(W, V) + + T = 30 + np.testing.assert_almost_equal(W, scenario.omega_b(T)) + np.testing.assert_almost_equal(V, scenario.velocity_b(T)) + np.testing.assert_almost_equal( + np.cross(W, V), scenario.acceleration_b(T)) + + # R = v/w, so test if loop crests at 2*R + R = v / w + T30 = scenario.pose(T) + np.testing.assert_almost_equal( + np.array([math.pi, 0, math.pi]), T30.rotation().xyz()) + self.assert_(gtsam.Point3(0, 0, 2 * R).equals(T30.translation(), 1e-9)) + + +if __name__ == '__main__': + unittest.main() diff --git a/cython/gtsam/utils/plot.py b/cython/gtsam/utils/plot.py new file mode 100644 index 000000000..19402a080 --- /dev/null +++ b/cython/gtsam/utils/plot.py @@ -0,0 +1,102 @@ +"""Various plotting utlities.""" + +import numpy as np +import matplotlib.pyplot as plt + + +def plot_pose2_on_axes(axes, pose, axis_length=0.1): + """Plot a 2D pose on given axis 'axes' with given 'axis_length'.""" + # get rotation and translation (center) + gRp = pose.rotation().matrix() # rotation from pose to global + t = pose.translation() + origin = np.array([t.x(), t.y()]) + + # draw the camera axes + x_axis = origin + gRp[:, 0] * axis_length + line = np.append(origin[np.newaxis], x_axis[np.newaxis], axis=0) + axes.plot(line[:, 0], line[:, 1], 'r-') + + y_axis = origin + gRp[:, 1] * axis_length + line = np.append(origin[np.newaxis], y_axis[np.newaxis], axis=0) + axes.plot(line[:, 0], line[:, 1], 'g-') + + +def plot_pose2(fignum, pose, axis_length=0.1): + """Plot a 2D pose on given figure with given 'axis_length'.""" + # get figure object + fig = plt.figure(fignum) + axes = fig.gca() + plot_pose2_on_axes(axes, pose, axis_length) + + +def plot_point3_on_axes(axes, point, linespec): + """Plot a 3D point on given axis 'axes' with given 'linespec'.""" + axes.plot([point.x()], [point.y()], [point.z()], linespec) + + +def plot_point3(fignum, point, linespec): + """Plot a 3D point on given figure with given 'linespec'.""" + fig = plt.figure(fignum) + axes = fig.gca(projection='3d') + plot_point3_on_axes(axes, point, linespec) + + +def plot_3d_points(fignum, values, linespec, marginals=None): + """ + Plots the Point3s in 'values', with optional covariances. + Finds all the Point3 objects in the given Values object and plots them. + If a Marginals object is given, this function will also plot marginal + covariance ellipses for each point. + """ + + keys = values.keys() + + # Plot points and covariance matrices + for i in range(keys.size()): + try: + p = values.atPoint3(keys.at(i)) + # if haveMarginals + # P = marginals.marginalCovariance(key); + # gtsam.plot_point3(p, linespec, P); + # else + plot_point3(fignum, p, linespec) + except RuntimeError: + continue + # I guess it's not a Point3 + + +def plot_pose3_on_axes(axes, pose, axis_length=0.1): + """Plot a 3D pose on given axis 'axes' with given 'axis_length'.""" + # get rotation and translation (center) + gRp = pose.rotation().matrix() # rotation from pose to global + t = pose.translation() + origin = np.array([t.x(), t.y(), t.z()]) + + # draw the camera axes + x_axis = origin + gRp[:, 0] * axis_length + line = np.append(origin[np.newaxis], x_axis[np.newaxis], axis=0) + axes.plot(line[:, 0], line[:, 1], line[:, 2], 'r-') + + y_axis = origin + gRp[:, 1] * axis_length + line = np.append(origin[np.newaxis], y_axis[np.newaxis], axis=0) + axes.plot(line[:, 0], line[:, 1], line[:, 2], 'g-') + + z_axis = origin + gRp[:, 2] * axis_length + line = np.append(origin[np.newaxis], z_axis[np.newaxis], axis=0) + axes.plot(line[:, 0], line[:, 1], line[:, 2], 'b-') + + # plot the covariance + # TODO (dellaert): make this work + # if (nargin>2) && (~isempty(P)) + # pPp = P(4:6,4:6); % covariance matrix in pose coordinate frame + # gPp = gRp*pPp*gRp'; % convert the covariance matrix to global coordinate frame + # gtsam.covarianceEllipse3D(origin,gPp); + # end + + +def plot_pose3(fignum, pose, axis_length=0.1): + """Plot a 3D pose on given figure with given 'axis_length'.""" + # get figure object + fig = plt.figure(fignum) + axes = fig.gca(projection='3d') + plot_pose3_on_axes(axes, pose, axis_length) diff --git a/cython/gtsam_eigency/eigency_cpp.h b/cython/gtsam_eigency/eigency_cpp.h index 45ac6caaa..923ca7c94 100644 --- a/cython/gtsam_eigency/eigency_cpp.h +++ b/cython/gtsam_eigency/eigency_cpp.h @@ -306,10 +306,17 @@ public: : cols), stride ) {} + + MapBase &operator=(const MatrixType &other) { + Base::operator=(other); + return *this; + } + + virtual ~MapBase() { } }; -template class DenseBase, +template class EigencyDenseBase, typename Scalar, int _Rows, int _Cols, int _Options = Eigen::AutoAlign | @@ -341,16 +348,18 @@ template class DenseBase, int _StrideOuter=0, int _StrideInner=0, int _MaxRows = _Rows, int _MaxCols = _Cols> -class FlattenedMap: public MapBase, _MapOptions, Eigen::Stride<_StrideOuter, _StrideInner> > { +class FlattenedMap: public MapBase, _MapOptions, Eigen::Stride<_StrideOuter, _StrideInner> > { public: - typedef MapBase, _MapOptions, Eigen::Stride<_StrideOuter, _StrideInner> > Base; + typedef MapBase, _MapOptions, Eigen::Stride<_StrideOuter, _StrideInner> > Base; FlattenedMap() - : Base(NULL, 0, 0) {} + : Base(NULL, 0, 0), + object_(NULL) {} FlattenedMap(Scalar *data, long rows, long cols, long outer_stride=0, long inner_stride=0) : Base(data, rows, cols, - Eigen::Stride<_StrideOuter, _StrideInner>(outer_stride, inner_stride)) { + Eigen::Stride<_StrideOuter, _StrideInner>(outer_stride, inner_stride)), + object_(NULL) { } FlattenedMap(PyArrayObject *object) @@ -359,18 +368,26 @@ public: (((PyArrayObject*)object)->nd == 2) ? ((PyArrayObject*)object)->dimensions[0] : 1, (((PyArrayObject*)object)->nd == 2) ? ((PyArrayObject*)object)->dimensions[1] : ((PyArrayObject*)object)->dimensions[0], Eigen::Stride<_StrideOuter, _StrideInner>(_StrideOuter != Eigen::Dynamic ? _StrideOuter : (((PyArrayObject*)object)->nd == 2) ? ((PyArrayObject*)object)->dimensions[0] : 1, - _StrideInner != Eigen::Dynamic ? _StrideInner : (((PyArrayObject*)object)->nd == 2) ? ((PyArrayObject*)object)->dimensions[1] : ((PyArrayObject*)object)->dimensions[0])) { + _StrideInner != Eigen::Dynamic ? _StrideInner : (((PyArrayObject*)object)->nd == 2) ? ((PyArrayObject*)object)->dimensions[1] : ((PyArrayObject*)object)->dimensions[0])), + object_(object) { if (((PyObject*)object != Py_None) && !PyArray_ISONESEGMENT(object)) throw std::invalid_argument("Numpy array must be a in one contiguous segment to be able to be transferred to a Eigen Map."); + + Py_XINCREF(object_); } FlattenedMap &operator=(const FlattenedMap &other) { - // Replace the memory that we point to (not a memory allocation) - new (this) FlattenedMap(const_cast(other.data()), - other.rows(), - other.cols(), - other.outerStride(), - other.innerStride()); + if (other.object_) { + new (this) FlattenedMap(other.object_); + } else { + // Replace the memory that we point to (not a memory allocation) + new (this) FlattenedMap(const_cast(other.data()), + other.rows(), + other.cols(), + other.outerStride(), + other.innerStride()); + } + return *this; } @@ -382,9 +399,16 @@ public: return static_cast(*this); } - operator DenseBase() const { - return DenseBase(static_cast(*this)); + operator EigencyDenseBase() const { + return EigencyDenseBase(static_cast(*this)); } + + virtual ~FlattenedMap() { + Py_XDECREF(object_); + } + +private: + PyArrayObject * const object_; }; @@ -394,39 +418,60 @@ public: typedef MapBase Base; typedef typename MatrixType::Scalar Scalar; + enum { + RowsAtCompileTime = Base::Base::RowsAtCompileTime, + ColsAtCompileTime = Base::Base::ColsAtCompileTime + }; + Map() - : Base(NULL, 0, 0) { + : Base(NULL, + (RowsAtCompileTime == Eigen::Dynamic) ? 0 : RowsAtCompileTime, + (ColsAtCompileTime == Eigen::Dynamic) ? 0 : ColsAtCompileTime), + object_(NULL) { } Map(Scalar *data, long rows, long cols) - : Base(data, rows, cols) {} + : Base(data, rows, cols), + object_(NULL) {} Map(PyArrayObject *object) : Base((PyObject*)object == Py_None? NULL: (Scalar *)object->data, // ROW: If array is in row-major order, transpose (see README) (PyObject*)object == Py_None? 0 : - (PyArray_IS_C_CONTIGUOUS(object) + (!PyArray_IS_F_CONTIGUOUS(object) ? ((object->nd == 1) ? 1 // ROW: If 1D row-major numpy array, set to 1 (row vector) : object->dimensions[1]) : object->dimensions[0]), // COLUMN: If array is in row-major order: transpose (see README) (PyObject*)object == Py_None? 0 : - (PyArray_IS_C_CONTIGUOUS(object) + (!PyArray_IS_F_CONTIGUOUS(object) ? object->dimensions[0] : ((object->nd == 1) ? 1 // COLUMN: If 1D col-major numpy array, set to length (column vector) - : object->dimensions[1]))) { + : object->dimensions[1]))), + object_(object) { if (((PyObject*)object != Py_None) && !PyArray_ISONESEGMENT(object)) throw std::invalid_argument("Numpy array must be a in one contiguous segment to be able to be transferred to a Eigen Map."); + Py_XINCREF(object_); } Map &operator=(const Map &other) { - // Replace the memory that we point to (not a memory allocation) - new (this) Map(const_cast(other.data()), - other.rows(), - other.cols()); + if (other.object_) { + new (this) Map(other.object_); + } else { + // Replace the memory that we point to (not a memory allocation) + new (this) Map(const_cast(other.data()), + other.rows(), + other.cols()); + } + + return *this; + } + + Map &operator=(const MatrixType &other) { + MapBase::operator=(other); return *this; } @@ -440,7 +485,14 @@ public: operator MatrixType() const { return MatrixType(static_cast(*this)); - } + } + + virtual ~Map() { + Py_XDECREF(object_); + } + +private: + PyArrayObject * const object_; }; diff --git a/examples/ImuFactorExample2.cpp b/examples/ImuFactorExample2.cpp new file mode 100644 index 000000000..b4ad5d574 --- /dev/null +++ b/examples/ImuFactorExample2.cpp @@ -0,0 +1,134 @@ + +#include +#include +#include +#include +#include +#include +#include +#include + +#include + +using namespace std; +using namespace gtsam; + +// Shorthand for velocity and pose variables +using symbol_shorthand::V; +using symbol_shorthand::X; + +const double kGravity = 9.81; + +/* ************************************************************************* */ +int main(int argc, char* argv[]) { + auto params = PreintegrationParams::MakeSharedU(kGravity); + params->setAccelerometerCovariance(I_3x3 * 0.1); + params->setGyroscopeCovariance(I_3x3 * 0.1); + params->setIntegrationCovariance(I_3x3 * 0.1); + params->setUse2ndOrderCoriolis(false); + params->setOmegaCoriolis(Vector3(0, 0, 0)); + + Pose3 delta(Rot3::Rodrigues(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20)); + + // Start with a camera on x-axis looking at origin + double radius = 30; + const Point3 up(0, 0, 1), target(0, 0, 0); + const Point3 position(radius, 0, 0); + const SimpleCamera camera = SimpleCamera::Lookat(position, target, up); + const auto pose_0 = camera.pose(); + + // Now, create a constant-twist scenario that makes the camera orbit the + // origin + double angular_velocity = M_PI, // rad/sec + delta_t = 1.0 / 18; // makes for 10 degrees per step + Vector3 angular_velocity_vector(0, -angular_velocity, 0); + Vector3 linear_velocity_vector(radius * angular_velocity, 0, 0); + auto scenario = ConstantTwistScenario(angular_velocity_vector, + linear_velocity_vector, pose_0); + + // Create a factor graph + NonlinearFactorGraph newgraph; + + // Create (incremental) ISAM2 solver + ISAM2 isam; + + // Create the initial estimate to the solution + // Intentionally initialize the variables off from the ground truth + Values initialEstimate, totalEstimate, result; + + // Add a prior on pose x0. This indirectly specifies where the origin is. + // 0.1 rad std on roll, pitch, yaw, 30cm std on x,y,z. + auto noise = noiseModel::Diagonal::Sigmas( + (Vector(6) << Vector3::Constant(0.1), Vector3::Constant(0.3)).finished()); + newgraph.push_back(PriorFactor(X(0), pose_0, noise)); + + // Add imu priors + Key biasKey = Symbol('b', 0); + auto biasnoise = noiseModel::Diagonal::Sigmas(Vector6::Constant(0.1)); + PriorFactor biasprior(biasKey, imuBias::ConstantBias(), + biasnoise); + newgraph.push_back(biasprior); + initialEstimate.insert(biasKey, imuBias::ConstantBias()); + auto velnoise = noiseModel::Diagonal::Sigmas(Vector3(0.1, 0.1, 0.1)); + + Vector n_velocity(3); + n_velocity << 0, angular_velocity * radius, 0; + PriorFactor velprior(V(0), n_velocity, velnoise); + newgraph.push_back(velprior); + + initialEstimate.insert(V(0), n_velocity); + + // IMU preintegrator + PreintegratedImuMeasurements accum(params); + + // Simulate poses and imu measurements, adding them to the factor graph + for (size_t i = 0; i < 36; ++i) { + double t = i * delta_t; + if (i == 0) { // First time add two poses + auto pose_1 = scenario.pose(delta_t); + initialEstimate.insert(X(0), pose_0.compose(delta)); + initialEstimate.insert(X(1), pose_1.compose(delta)); + } else if (i >= 2) { // Add more poses as necessary + auto pose_i = scenario.pose(t); + initialEstimate.insert(X(i), pose_i.compose(delta)); + } + + if (i > 0) { + // Add Bias variables periodically + if (i % 5 == 0) { + biasKey++; + Symbol b1 = biasKey - 1; + Symbol b2 = biasKey; + Vector6 covvec; + covvec << 0.1, 0.1, 0.1, 0.1, 0.1, 0.1; + auto cov = noiseModel::Diagonal::Variances(covvec); + auto f = boost::make_shared >( + b1, b2, imuBias::ConstantBias(), cov); + newgraph.add(f); + initialEstimate.insert(biasKey, imuBias::ConstantBias()); + } + // Predict acceleration and gyro measurements in (actual) body frame + auto measuredAcc = scenario.acceleration_b(t) - + scenario.rotation(t).transpose() * params->n_gravity; + auto measuredOmega = scenario.omega_b(t); + accum.integrateMeasurement(measuredAcc, measuredOmega, delta_t); + + // Add Imu Factor + ImuFactor imufac(X(i - 1), V(i - 1), X(i), V(i), biasKey, accum); + newgraph.add(imufac); + + // insert new velocity, which is wrong + initialEstimate.insert(V(i), n_velocity); + accum.resetIntegration(); + } + + // Incremental solution + isam.update(newgraph, initialEstimate); + result = isam.calculateEstimate(); + newgraph = NonlinearFactorGraph(); + initialEstimate.clear(); + } + GTSAM_PRINT(result); + return 0; +} +/* ************************************************************************* */ diff --git a/gtsam.h b/gtsam.h index d0be06652..ca37d2d62 100644 --- a/gtsam.h +++ b/gtsam.h @@ -568,7 +568,7 @@ class Pose2 { static gtsam::Pose2 Expmap(Vector v); static Vector Logmap(const gtsam::Pose2& p); Matrix AdjointMap() const; - Vector Adjoint(const Vector& xi) const; + Vector Adjoint(Vector xi) const; static Matrix wedge(double vx, double vy, double w); // Group Actions on Point2 @@ -865,7 +865,7 @@ class CalibratedCamera { // Standard Constructors and Named Constructors CalibratedCamera(); CalibratedCamera(const gtsam::Pose3& pose); - CalibratedCamera(const Vector& v); + CalibratedCamera(Vector v); static gtsam::CalibratedCamera Level(const gtsam::Pose2& pose2, double height); // Testable @@ -875,7 +875,7 @@ class CalibratedCamera { // Manifold static size_t Dim(); size_t dim() const; - gtsam::CalibratedCamera retract(const Vector& d) const; + gtsam::CalibratedCamera retract(Vector d) const; Vector localCoordinates(const gtsam::CalibratedCamera& T2) const; // Action on Point3 @@ -911,7 +911,7 @@ class PinholeCamera { CALIBRATION calibration() const; // Manifold - This retract(const Vector& d) const; + This retract(Vector d) const; Vector localCoordinates(const This& T2) const; size_t dim() const; static size_t Dim(); @@ -938,6 +938,8 @@ virtual class SimpleCamera { static gtsam::SimpleCamera Level(const gtsam::Pose2& pose, double height); static gtsam::SimpleCamera Lookat(const gtsam::Point3& eye, const gtsam::Point3& target, const gtsam::Point3& upVector, const gtsam::Cal3_S2& K); + static gtsam::SimpleCamera Lookat(const gtsam::Point3& eye, const gtsam::Point3& target, + const gtsam::Point3& upVector); // Testable void print(string s) const; @@ -948,7 +950,7 @@ virtual class SimpleCamera { gtsam::Cal3_S2 calibration() const; // Manifold - gtsam::SimpleCamera retract(const Vector& d) const; + gtsam::SimpleCamera retract(Vector d) const; Vector localCoordinates(const gtsam::SimpleCamera& T2) const; size_t dim() const; static size_t Dim(); @@ -990,7 +992,7 @@ class StereoCamera { gtsam::Cal3_S2Stereo calibration() const; // Manifold - gtsam::StereoCamera retract(const Vector& d) const; + gtsam::StereoCamera retract(Vector d) const; Vector localCoordinates(const gtsam::StereoCamera& T2) const; size_t dim() const; static size_t Dim(); @@ -1225,12 +1227,12 @@ virtual class Diagonal : gtsam::noiseModel::Gaussian { }; virtual class Constrained : gtsam::noiseModel::Diagonal { - static gtsam::noiseModel::Constrained* MixedSigmas(const Vector& mu, const Vector& sigmas); - static gtsam::noiseModel::Constrained* MixedSigmas(double m, const Vector& sigmas); - static gtsam::noiseModel::Constrained* MixedVariances(const Vector& mu, const Vector& variances); - static gtsam::noiseModel::Constrained* MixedVariances(const Vector& variances); - static gtsam::noiseModel::Constrained* MixedPrecisions(const Vector& mu, const Vector& precisions); - static gtsam::noiseModel::Constrained* MixedPrecisions(const Vector& precisions); + static gtsam::noiseModel::Constrained* MixedSigmas(Vector mu, Vector sigmas); + static gtsam::noiseModel::Constrained* MixedSigmas(double m, Vector sigmas); + static gtsam::noiseModel::Constrained* MixedVariances(Vector mu, Vector variances); + static gtsam::noiseModel::Constrained* MixedVariances(Vector variances); + static gtsam::noiseModel::Constrained* MixedPrecisions(Vector mu, Vector precisions); + static gtsam::noiseModel::Constrained* MixedPrecisions(Vector precisions); static gtsam::noiseModel::Constrained* All(size_t dim); static gtsam::noiseModel::Constrained* All(size_t dim, double mu); @@ -1413,12 +1415,12 @@ virtual class JacobianFactor : gtsam::GaussianFactor { pair jacobianUnweighted() const; Matrix augmentedJacobianUnweighted() const; - void transposeMultiplyAdd(double alpha, const Vector& e, gtsam::VectorValues& x) const; + void transposeMultiplyAdd(double alpha, Vector e, gtsam::VectorValues& x) const; gtsam::JacobianFactor whiten() const; pair eliminate(const gtsam::Ordering& keys) const; - void setModel(bool anyConstrained, const Vector& sigmas); + void setModel(bool anyConstrained, Vector sigmas); gtsam::noiseModel::Diagonal* get_model() const; @@ -2502,6 +2504,11 @@ class NavState { #include virtual class PreintegratedRotationParams { PreintegratedRotationParams(); + + // Testable + void print(string s) const; + bool equals(const gtsam::PreintegratedRotationParams& expected, double tol); + void setGyroscopeCovariance(Matrix cov); void setOmegaCoriolis(Vector omega); void setBodyPSensor(const gtsam::Pose3& pose); @@ -2509,13 +2516,23 @@ virtual class PreintegratedRotationParams { Matrix getGyroscopeCovariance() const; // TODO(frank): allow optional - // boost::optional getOmegaCoriolis() const; + // boost::optional getOmegaCoriolis() const; // boost::optional getBodyPSensor() const; }; #include virtual class PreintegrationParams : gtsam::PreintegratedRotationParams { PreintegrationParams(Vector n_gravity); + + static gtsam::PreintegrationParams* MakeSharedD(double g); + static gtsam::PreintegrationParams* MakeSharedU(double g); + static gtsam::PreintegrationParams* MakeSharedD(); // default g = 9.81 + static gtsam::PreintegrationParams* MakeSharedU(); // default g = 9.81 + + // Testable + void print(string s) const; + bool equals(const gtsam::PreintegrationParams& expected, double tol); + void setAccelerometerCovariance(Matrix cov); void setIntegrationCovariance(Matrix cov); void setUse2ndOrderCoriolis(bool flag); @@ -2523,7 +2540,6 @@ virtual class PreintegrationParams : gtsam::PreintegratedRotationParams { Matrix getAccelerometerCovariance() const; Matrix getIntegrationCovariance() const; bool getUse2ndOrderCoriolis() const; - void print(string s) const; }; #include @@ -2649,10 +2665,12 @@ virtual class Rot3AttitudeFactor : gtsam::NonlinearFactor{ gtsam::Unit3 bRef() const; }; -virtual class Pose3AttitudeFactor : gtsam::NonlinearFactor{ - Pose3AttitudeFactor(size_t key, const gtsam::Unit3& nZ, const gtsam::noiseModel::Diagonal* model, - const gtsam::Unit3& bRef); - Pose3AttitudeFactor(size_t key, const gtsam::Unit3& nZ, const gtsam::noiseModel::Diagonal* model); +virtual class Pose3AttitudeFactor : gtsam::NonlinearFactor { + Pose3AttitudeFactor(size_t key, const gtsam::Unit3& nZ, + const gtsam::noiseModel::Diagonal* model, + const gtsam::Unit3& bRef); + Pose3AttitudeFactor(size_t key, const gtsam::Unit3& nZ, + const gtsam::noiseModel::Diagonal* model); Pose3AttitudeFactor(); void print(string s) const; bool equals(const gtsam::NonlinearFactor& expected, double tol) const; @@ -2660,6 +2678,30 @@ virtual class Pose3AttitudeFactor : gtsam::NonlinearFactor{ gtsam::Unit3 bRef() const; }; +#include +virtual class Scenario { + gtsam::Pose3 pose(double t) const; + Vector omega_b(double t) const; + Vector velocity_n(double t) const; + Vector acceleration_n(double t) const; + gtsam::Rot3 rotation(double t) const; + gtsam::NavState navState(double t) const; + Vector velocity_b(double t) const; + Vector acceleration_b(double t) const; +}; + +virtual class ConstantTwistScenario : gtsam::Scenario { + ConstantTwistScenario(Vector w, Vector v); + ConstantTwistScenario(Vector w, Vector v, + const Pose3& nTb0); +}; + +virtual class AcceleratingScenario : gtsam::Scenario { + AcceleratingScenario(const gtsam::Rot3& nRb, const gtsam::Point3& p0, + Vector v0, Vector a_n, + Vector omega_b); +}; + //************************************************************************* // Utilities //************************************************************************* diff --git a/gtsam/geometry/Unit3.cpp b/gtsam/geometry/Unit3.cpp old mode 100644 new mode 100755 index e0e0ecb56..08f14c829 --- a/gtsam/geometry/Unit3.cpp +++ b/gtsam/geometry/Unit3.cpp @@ -65,69 +65,76 @@ Unit3 Unit3::Random(boost::mt19937 & rng) { return Unit3(d[0], d[1], d[2]); } +/* ************************************************************************* */ +// Get the axis of rotation with the minimum projected length of the point +static Point3 CalculateBestAxis(const Point3& n) { + double mx = fabs(n.x()), my = fabs(n.y()), mz = fabs(n.z()); + if ((mx <= my) && (mx <= mz)) { + return Point3(1.0, 0.0, 0.0); + } else if ((my <= mx) && (my <= mz)) { + return Point3(0.0, 1.0, 0.0); + } else { + return Point3(0, 0, 1); + } +} + /* ************************************************************************* */ const Matrix32& Unit3::basis(OptionalJacobian<6, 2> H) const { #ifdef GTSAM_USE_TBB - // NOTE(hayk): At some point it seemed like this reproducably resulted in deadlock. However, I - // can't see the reason why and I can no longer reproduce it. It may have been a red herring, or - // there is still a latent bug to watch out for. + // NOTE(hayk): At some point it seemed like this reproducably resulted in + // deadlock. However, I don't know why and I can no longer reproduce it. + // It either was a red herring or there is still a latent bug left to debug. tbb::mutex::scoped_lock lock(B_mutex_); #endif - // Return cached basis if available and the Jacobian isn't needed. - if (B_ && !H) { - return *B_; - } - - // Return cached basis and derivatives if available. - if (B_ && H && H_B_) { - *H = *H_B_; - return *B_; - } - - // Get the unit vector and derivative wrt this. - // NOTE(hayk): We can't call point3(), because it would recursively call basis(). - const Point3 n(p_); - - // Get the axis of rotation with the minimum projected length of the point - Point3 axis(0, 0, 1); - double mx = fabs(n.x()), my = fabs(n.y()), mz = fabs(n.z()); - if ((mx <= my) && (mx <= mz)) { - axis = Point3(1.0, 0.0, 0.0); - } else if ((my <= mx) && (my <= mz)) { - axis = Point3(0.0, 1.0, 0.0); - } - - // Choose the direction of the first basis vector b1 in the tangent plane by crossing n with - // the chosen axis. - Matrix33 H_B1_n; - Point3 B1 = gtsam::cross(n, axis, H ? &H_B1_n : nullptr); - - // Normalize result to get a unit vector: b1 = B1 / |B1|. - Matrix33 H_b1_B1; - Point3 b1 = normalize(B1, H ? &H_b1_B1 : nullptr); - - // Get the second basis vector b2, which is orthogonal to n and b1, by crossing them. - // No need to normalize this, p and b1 are orthogonal unit vectors. - Matrix33 H_b2_n, H_b2_b1; - Point3 b2 = gtsam::cross(n, b1, H ? &H_b2_n : nullptr, H ? &H_b2_b1 : nullptr); - - // Create the basis by stacking b1 and b2. - B_.reset(Matrix32()); - (*B_) << b1.x(), b2.x(), b1.y(), b2.y(), b1.z(), b2.z(); + const bool cachedBasis = static_cast(B_); + const bool cachedJacobian = static_cast(H_B_); if (H) { - // Chain rule tomfoolery to compute the derivative. - const Matrix32& H_n_p = *B_; - const Matrix32 H_b1_p = H_b1_B1 * H_B1_n * H_n_p; - const Matrix32 H_b2_p = H_b2_n * H_n_p + H_b2_b1 * H_b1_p; + if (!cachedJacobian) { + // Compute Jacobian. Recomputes B_ + Matrix32 B; + Matrix62 jacobian; + Matrix33 H_B1_n, H_b1_B1, H_b2_n, H_b2_b1; - // Cache the derivative and fill the result. - H_B_.reset(Matrix62()); - (*H_B_) << H_b1_p, H_b2_p; + // Choose the direction of the first basis vector b1 in the tangent plane + // by crossing n with the chosen axis. + const Point3 n(p_), axis = CalculateBestAxis(n); + const Point3 B1 = gtsam::cross(n, axis, &H_B1_n); + + // Normalize result to get a unit vector: b1 = B1 / |B1|. + B.col(0) = normalize(B1, &H_b1_B1); + + // Get the second basis vector b2, which is orthogonal to n and b1. + B.col(1) = gtsam::cross(n, B.col(0), &H_b2_n, &H_b2_b1); + + // Chain rule tomfoolery to compute the jacobian. + const Matrix32& H_n_p = B; + jacobian.block<3, 2>(0, 0) = H_b1_B1 * H_B1_n * H_n_p; + auto H_b1_p = jacobian.block<3, 2>(0, 0); + jacobian.block<3, 2>(3, 0) = H_b2_n * H_n_p + H_b2_b1 * H_b1_p; + + // Cache the result and jacobian + H_B_.reset(jacobian); + B_.reset(B); + } + + // Return cached jacobian, possibly computed just above *H = *H_B_; } + if (!cachedBasis) { + // Same calculation as above, without derivatives. + // Done after H block, as that possibly computes B_ for the first time + Matrix32 B; + + const Point3 n(p_), axis = CalculateBestAxis(n); + const Point3 B1 = gtsam::cross(n, axis); + B.col(0) = normalize(B1); + B.col(1) = gtsam::cross(n, B.col(0)); + B_.reset(B); + } + return *B_; } @@ -257,7 +264,7 @@ Unit3 Unit3::retract(const Vector2& v) const { std::cos(theta) * p_ + xi_hat * (sin(theta) / theta); return Unit3(exp_p_xi_hat); } - + /* ************************************************************************* */ Vector2 Unit3::localCoordinates(const Unit3& other) const { const double x = p_.dot(other.p_); diff --git a/gtsam/geometry/tests/testUnit3.cpp b/gtsam/geometry/tests/testUnit3.cpp index 71093d668..542aca038 100644 --- a/gtsam/geometry/tests/testUnit3.cpp +++ b/gtsam/geometry/tests/testUnit3.cpp @@ -314,15 +314,24 @@ TEST(Unit3, basis) { Unit3 p(0.1, -0.2, 0.9); Matrix expected(3, 2); - expected << 0.0, -0.994169047, 0.97618706, - -0.0233922129, 0.216930458, 0.105264958; + expected << 0.0, -0.994169047, 0.97618706, -0.0233922129, 0.216930458, 0.105264958; Matrix62 actualH; - Matrix actual = p.basis(actualH); - EXPECT(assert_equal(expected, actual, 1e-6)); - Matrix62 expectedH = numericalDerivative11( - boost::bind(BasisTest, _1, boost::none), p); + boost::bind(BasisTest, _1, boost::none), p); + + // without H, first time + EXPECT(assert_equal(expected, p.basis(), 1e-6)); + + // without H, cached + EXPECT(assert_equal(expected, p.basis(), 1e-6)); + + // with H, first time + EXPECT(assert_equal(expected, p.basis(actualH), 1e-6)); + EXPECT(assert_equal(expectedH, actualH, 1e-8)); + + // with H, cached + EXPECT(assert_equal(expected, p.basis(actualH), 1e-6)); EXPECT(assert_equal(expectedH, actualH, 1e-8)); } @@ -432,7 +441,8 @@ TEST(Unit3, ErrorBetweenFactor) { // Add process factors using the dot product error function. SharedNoiseModel R_process = noiseModel::Isotropic::Sigma(2, 0.01); for (size_t i = 0; i < data.size() - 1; i++) { - Expression exp(Expression(U(i)), &Unit3::errorVector, Expression(U(i + 1))); + Expression exp(Expression(U(i)), &Unit3::errorVector, + Expression(U(i + 1))); graph.addExpressionFactor(R_process, Vector2::Zero(), exp); } diff --git a/gtsam/inference/Symbol.h b/gtsam/inference/Symbol.h index 2d7ede8e7..6b5bfcc03 100644 --- a/gtsam/inference/Symbol.h +++ b/gtsam/inference/Symbol.h @@ -26,10 +26,9 @@ namespace gtsam { /** - * Character and index key used in VectorValues, GaussianFactorGraph, - * GaussianFactor, etc. These keys are generated at runtime from TypedSymbol - * keys when linearizing a nonlinear factor graph. This key is not type - * safe, so cannot be used with any Nonlinear* classes. + * Character and index key used to refer to variables. Will simply cast to a Key, + * i.e., a large integer. Keys are used to retrieve values from Values, + * specify what variables factors depend on, etc. */ class GTSAM_EXPORT Symbol { protected: diff --git a/gtsam/linear/Scatter.cpp b/gtsam/linear/Scatter.cpp index db5c25442..5312da34b 100644 --- a/gtsam/linear/Scatter.cpp +++ b/gtsam/linear/Scatter.cpp @@ -70,9 +70,6 @@ Scatter::Scatter(const GaussianFactorGraph& gfg, iterator first = begin(); if (ordering) first += ordering->size(); if (first != end()) std::sort(first, end()); - - // Filter out keys with zero dimensions (if ordering had more keys) - erase(std::remove_if(begin(), end(), SlotEntry::Zero), end()); } /* ************************************************************************* */ diff --git a/gtsam/navigation/Scenario.h b/gtsam/navigation/Scenario.h index e4b380ff9..acb8f46f5 100644 --- a/gtsam/navigation/Scenario.h +++ b/gtsam/navigation/Scenario.h @@ -57,10 +57,13 @@ class Scenario { class ConstantTwistScenario : public Scenario { public: /// Construct scenario with constant twist [w,v] - ConstantTwistScenario(const Vector3& w, const Vector3& v) - : twist_((Vector6() << w, v).finished()), a_b_(w.cross(v)) {} + ConstantTwistScenario(const Vector3& w, const Vector3& v, + const Pose3& nTb0 = Pose3()) + : twist_((Vector6() << w, v).finished()), a_b_(w.cross(v)), nTb0_(nTb0) {} - Pose3 pose(double t) const override { return Pose3::Expmap(twist_ * t); } + Pose3 pose(double t) const override { + return nTb0_ * Pose3::Expmap(twist_ * t); + } Vector3 omega_b(double t) const override { return twist_.head<3>(); } Vector3 velocity_n(double t) const override { return rotation(t).matrix() * twist_.tail<3>(); @@ -70,6 +73,7 @@ class ConstantTwistScenario : public Scenario { private: const Vector6 twist_; const Vector3 a_b_; // constant centripetal acceleration in body = w_b * v_b + const Pose3 nTb0_; }; /// Accelerating from an arbitrary initial state, with optional rotation diff --git a/gtsam/navigation/tests/testScenario.cpp b/gtsam/navigation/tests/testScenario.cpp index bc965b058..161b8841a 100644 --- a/gtsam/navigation/tests/testScenario.cpp +++ b/gtsam/navigation/tests/testScenario.cpp @@ -15,8 +15,8 @@ * @author Frank Dellaert */ -#include #include +#include #include #include @@ -96,9 +96,33 @@ TEST(Scenario, Loop) { const double R = v / w; const Pose3 T30 = scenario.pose(30); EXPECT(assert_equal(Rot3::Rodrigues(0, M_PI, 0), T30.rotation(), 1e-9)); + EXPECT(assert_equal(Vector3(M_PI, 0, M_PI), T30.rotation().xyz())); EXPECT(assert_equal(Point3(0, 0, 2 * R), T30.translation(), 1e-9)); } +/* ************************************************************************* */ +TEST(Scenario, LoopWithInitialPose) { + // Forward velocity 2m/s + // Pitch up with angular velocity 6 kDegree/sec (negative in FLU) + const double v = 2, w = 6 * kDegree; + const Vector3 W(0, -w, 0), V(v, 0, 0); + const Rot3 nRb0 = Rot3::yaw(M_PI); + const Pose3 nTb0(nRb0, Point3(1, 2, 3)); + const ConstantTwistScenario scenario(W, V, nTb0); + + const double T = 30; + EXPECT(assert_equal(W, scenario.omega_b(T), 1e-9)); + EXPECT(assert_equal(V, scenario.velocity_b(T), 1e-9)); + EXPECT(assert_equal(W.cross(V), scenario.acceleration_b(T), 1e-9)); + + // R = v/w, so test if loop crests at 2*R + const double R = v / w; + const Pose3 T30 = scenario.pose(30); + EXPECT( + assert_equal(nRb0 * Rot3::Rodrigues(0, M_PI, 0), T30.rotation(), 1e-9)); + EXPECT(assert_equal(Point3(1, 2, 3 + 2 * R), T30.translation(), 1e-9)); +} + /* ************************************************************************* */ TEST(Scenario, Accelerating) { // Set up body pointing towards y axis, and start at 10,20,0 with velocity diff --git a/gtsam/nonlinear/internal/LevenbergMarquardtState.h b/gtsam/nonlinear/internal/LevenbergMarquardtState.h index 19a390c45..bd5465dda 100644 --- a/gtsam/nonlinear/internal/LevenbergMarquardtState.h +++ b/gtsam/nonlinear/internal/LevenbergMarquardtState.h @@ -113,9 +113,9 @@ struct LevenbergMarquardtState : public NonlinearOptimizerState { // Small cache of A|b|model indexed by dimension. Can save many mallocs. mutable std::vector noiseModelCache; CachedModel* getCachedModel(size_t dim) const { - if (dim > noiseModelCache.size()) - noiseModelCache.resize(dim); - CachedModel* item = &noiseModelCache[dim - 1]; + if (dim >= noiseModelCache.size()) + noiseModelCache.resize(dim+1); + CachedModel* item = &noiseModelCache[dim]; if (!item->model) *item = CachedModel(dim, 1.0 / std::sqrt(lambda)); return item; diff --git a/gtsam_unstable/slam/SmartRangeFactor.h b/gtsam_unstable/slam/SmartRangeFactor.h index 3164b360e..a844ee823 100644 --- a/gtsam_unstable/slam/SmartRangeFactor.h +++ b/gtsam_unstable/slam/SmartRangeFactor.h @@ -121,17 +121,18 @@ class SmartRangeFactor: public NoiseModelFactor { // use best fh to find actual intersection points if (bestCircle2 && best_fh) { - std::list intersections = circleCircleIntersection( - circle1.center, bestCircle2->center, best_fh); + auto bestCircleCenter = bestCircle2->center; + std::list intersections = + circleCircleIntersection(circle1.center, bestCircleCenter, best_fh); - // pick winner based on other measurements - double error1 = 0, error2 = 0; - Point2 p1 = intersections.front(), p2 = intersections.back(); - for (const Circle2& it : circles) { - error1 += distance2(it.center, p1); - error2 += distance2(it.center, p2); - } - return (error1 < error2) ? p1 : p2; + // pick winner based on other measurements + double error1 = 0, error2 = 0; + Point2 p1 = intersections.front(), p2 = intersections.back(); + for (const Circle2& it : circles) { + error1 += distance2(it.center, p1); + error2 += distance2(it.center, p2); + } + return (error1 < error2) ? p1 : p2; } else { throw std::runtime_error("triangulate failed"); } diff --git a/wrap/Method.cpp b/wrap/Method.cpp index 2637275d1..1a58692b6 100644 --- a/wrap/Method.cpp +++ b/wrap/Method.cpp @@ -106,7 +106,7 @@ void Method::emit_cython_pyx_no_overload(FileWriter& file, // leverage python's special treatment for print if (funcName == "print_") { - file.oss << " def __str__(self):\n"; + file.oss << " def __repr__(self):\n"; file.oss << " strBuf = RedirectCout()\n"; file.oss << " self.print_('')\n"; file.oss << " return strBuf.str()\n";