Merge branch 'feature/cython-examples' into fix/ImuFactor
Frank Dellaert
commit
0219b39341
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@ -52,7 +52,7 @@ function(pyx_to_cpp target pyx_file generated_cpp include_dirs)
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add_custom_command(
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OUTPUT ${generated_cpp}
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COMMAND
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${CYTHON_EXECUTABLE} -X boundscheck=False -a -v --fast-fail --cplus ${includes_for_cython} ${pyx_file} -o ${generated_cpp}
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${CYTHON_EXECUTABLE} -X boundscheck=False -v --fast-fail --cplus ${includes_for_cython} ${pyx_file} -o ${generated_cpp}
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VERBATIM)
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add_custom_target(${target} ALL DEPENDS ${generated_cpp})
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endfunction()
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@ -95,7 +95,7 @@ function(cythonize target pyx_file output_lib_we output_dir include_dirs libs in
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# Late dependency injection, to make sure this gets called whenever the interface header is updated
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# See: https://stackoverflow.com/questions/40032593/cmake-does-not-rebuild-dependent-after-prerequisite-changes
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add_custom_command(OUTPUT ${generated_cpp} DEPENDS ${interface_header} APPEND)
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add_custom_command(OUTPUT ${generated_cpp} DEPENDS ${interface_header} ${pyx_file} APPEND)
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if (NOT "${dependencies}" STREQUAL "")
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add_dependencies(${target}_pyx2cpp "${dependencies}")
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endif()
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@ -0,0 +1,52 @@
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"""
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GTSAM Copyright 2010-2018, Georgia Tech Research Corporation,
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Atlanta, Georgia 30332-0415
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All Rights Reserved
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Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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See LICENSE for the license information
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Simple robot motion example, with prior and two odometry measurements
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Author: Frank Dellaert
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"""
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# pylint: disable=invalid-name, E1101
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from __future__ import print_function
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import numpy as np
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import gtsam
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# Create noise models
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ODOMETRY_NOISE = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.2, 0.2, 0.1]))
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PRIOR_NOISE = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.3, 0.3, 0.1]))
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# Create an empty nonlinear factor graph
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graph = gtsam.NonlinearFactorGraph()
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# Add a prior on the first pose, setting it to the origin
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# A prior factor consists of a mean and a noise model (covariance matrix)
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priorMean = gtsam.Pose2(0.0, 0.0, 0.0) # prior at origin
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graph.add(gtsam.PriorFactorPose2(1, priorMean, PRIOR_NOISE))
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# Add odometry factors
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odometry = gtsam.Pose2(2.0, 0.0, 0.0)
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# For simplicity, we will use the same noise model for each odometry factor
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# Create odometry (Between) factors between consecutive poses
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graph.add(gtsam.BetweenFactorPose2(1, 2, odometry, ODOMETRY_NOISE))
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graph.add(gtsam.BetweenFactorPose2(2, 3, odometry, ODOMETRY_NOISE))
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graph.print_("\nFactor Graph:\n")
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# Create the data structure to hold the initialEstimate estimate to the solution
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# For illustrative purposes, these have been deliberately set to incorrect values
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initial = gtsam.Values()
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initial.insert(1, gtsam.Pose2(0.5, 0.0, 0.2))
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initial.insert(2, gtsam.Pose2(2.3, 0.1, -0.2))
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initial.insert(3, gtsam.Pose2(4.1, 0.1, 0.1))
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initial.print_("\nInitial Estimate:\n")
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# optimize using Levenberg-Marquardt optimization
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params = gtsam.LevenbergMarquardtParams()
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optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initial, params)
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result = optimizer.optimize()
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result.print_("\nFinal Result:\n")
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@ -0,0 +1,80 @@
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"""
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GTSAM Copyright 2010-2018, Georgia Tech Research Corporation,
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Atlanta, Georgia 30332-0415
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All Rights Reserved
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Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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See LICENSE for the license information
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Simple robotics example using odometry measurements and bearing-range (laser) measurements
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Author: Alex Cunningham (C++), Kevin Deng & Frank Dellaert (Python)
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"""
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# pylint: disable=invalid-name, E1101
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from __future__ import print_function
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import numpy as np
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import gtsam
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# Create noise models
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PRIOR_NOISE = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.3, 0.3, 0.1]))
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ODOMETRY_NOISE = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.2, 0.2, 0.1]))
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MEASUREMENT_NOISE = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.1, 0.2]))
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# Create an empty nonlinear factor graph
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graph = gtsam.NonlinearFactorGraph()
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# Create the keys corresponding to unknown variables in the factor graph
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X1 = gtsam.symbol(ord('x'), 1)
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X2 = gtsam.symbol(ord('x'), 2)
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X3 = gtsam.symbol(ord('x'), 3)
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L1 = gtsam.symbol(ord('l'), 4)
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L2 = gtsam.symbol(ord('l'), 5)
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# Add a prior on pose X1 at the origin. A prior factor consists of a mean and a noise model
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graph.add(gtsam.PriorFactorPose2(X1, gtsam.Pose2(0.0, 0.0, 0.0), PRIOR_NOISE))
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# Add odometry factors between X1,X2 and X2,X3, respectively
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graph.add(gtsam.BetweenFactorPose2(
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X1, X2, gtsam.Pose2(2.0, 0.0, 0.0), ODOMETRY_NOISE))
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graph.add(gtsam.BetweenFactorPose2(
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X2, X3, gtsam.Pose2(2.0, 0.0, 0.0), ODOMETRY_NOISE))
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# Add Range-Bearing measurements to two different landmarks L1 and L2
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graph.add(gtsam.BearingRangeFactor2D(
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X1, L1, gtsam.Rot2.fromDegrees(45), np.sqrt(4.0+4.0), MEASUREMENT_NOISE))
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graph.add(gtsam.BearingRangeFactor2D(
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X2, L1, gtsam.Rot2.fromDegrees(90), 2.0, MEASUREMENT_NOISE))
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graph.add(gtsam.BearingRangeFactor2D(
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X3, L2, gtsam.Rot2.fromDegrees(90), 2.0, MEASUREMENT_NOISE))
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# Print graph
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graph.print_("Factor Graph:\n")
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# Create (deliberately inaccurate) initial estimate
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initial_estimate = gtsam.Values()
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initial_estimate.insert(X1, gtsam.Pose2(-0.25, 0.20, 0.15))
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initial_estimate.insert(X2, gtsam.Pose2(2.30, 0.10, -0.20))
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initial_estimate.insert(X3, gtsam.Pose2(4.10, 0.10, 0.10))
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initial_estimate.insert(L1, gtsam.Point2(1.80, 2.10))
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initial_estimate.insert(L2, gtsam.Point2(4.10, 1.80))
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# Print
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initial_estimate.print_("Initial Estimate:\n")
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# Optimize using Levenberg-Marquardt optimization. The optimizer
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# accepts an optional set of configuration parameters, controlling
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# things like convergence criteria, the type of linear system solver
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# to use, and the amount of information displayed during optimization.
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# Here we will use the default set of parameters. See the
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# documentation for the full set of parameters.
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params = gtsam.LevenbergMarquardtParams()
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optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initial_estimate, params)
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result = optimizer.optimize()
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result.print_("\nFinal Result:\n")
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# Calculate and print marginal covariances for all variables
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marginals = gtsam.Marginals(graph, result)
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for (key, str) in [(X1, "X1"), (X2, "X2"), (X3, "X3"), (L1, "L1"), (L2, "L2")]:
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print("{} covariance:\n{}\n".format(str, marginals.marginalCovariance(key)))
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@ -0,0 +1,87 @@
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"""
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GTSAM Copyright 2010-2018, Georgia Tech Research Corporation,
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Atlanta, Georgia 30332-0415
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All Rights Reserved
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Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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See LICENSE for the license information
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Simple robotics example using odometry measurements and bearing-range (laser) measurements
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Author: Alex Cunningham (C++), Kevin Deng & Frank Dellaert (Python)
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"""
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# pylint: disable=invalid-name, E1101
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from __future__ import print_function
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import math
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import numpy as np
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import gtsam
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def vector3(x, y, z):
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"""Create 3d double numpy array."""
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return np.array([x, y, z], dtype=np.float)
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# Create noise models
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PRIOR_NOISE = gtsam.noiseModel_Diagonal.Sigmas(vector3(0.3, 0.3, 0.1))
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ODOMETRY_NOISE = gtsam.noiseModel_Diagonal.Sigmas(vector3(0.2, 0.2, 0.1))
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# 1. Create a factor graph container and add factors to it
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graph = gtsam.NonlinearFactorGraph()
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# 2a. Add a prior on the first pose, setting it to the origin
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# A prior factor consists of a mean and a noise ODOMETRY_NOISE (covariance matrix)
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graph.add(gtsam.PriorFactorPose2(1, gtsam.Pose2(0, 0, 0), PRIOR_NOISE))
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# 2b. Add odometry factors
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# Create odometry (Between) factors between consecutive poses
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graph.add(gtsam.BetweenFactorPose2(1, 2, gtsam.Pose2(2, 0, 0), ODOMETRY_NOISE))
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graph.add(gtsam.BetweenFactorPose2(
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2, 3, gtsam.Pose2(2, 0, math.pi / 2), ODOMETRY_NOISE))
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graph.add(gtsam.BetweenFactorPose2(
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3, 4, gtsam.Pose2(2, 0, math.pi / 2), ODOMETRY_NOISE))
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graph.add(gtsam.BetweenFactorPose2(
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4, 5, gtsam.Pose2(2, 0, math.pi / 2), ODOMETRY_NOISE))
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# 2c. Add the loop closure constraint
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# This factor encodes the fact that we have returned to the same pose. In real
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# systems, these constraints may be identified in many ways, such as appearance-based
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# techniques with camera images. We will use another Between Factor to enforce this constraint:
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graph.add(gtsam.BetweenFactorPose2(
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5, 2, gtsam.Pose2(2, 0, math.pi / 2), ODOMETRY_NOISE))
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graph.print_("\nFactor Graph:\n") # print
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# 3. Create the data structure to hold the initial_estimate estimate to the
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# solution. For illustrative purposes, these have been deliberately set to incorrect values
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initial_estimate = gtsam.Values()
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initial_estimate.insert(1, gtsam.Pose2(0.5, 0.0, 0.2))
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initial_estimate.insert(2, gtsam.Pose2(2.3, 0.1, -0.2))
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initial_estimate.insert(3, gtsam.Pose2(4.1, 0.1, math.pi / 2))
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initial_estimate.insert(4, gtsam.Pose2(4.0, 2.0, math.pi))
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initial_estimate.insert(5, gtsam.Pose2(2.1, 2.1, -math.pi / 2))
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initial_estimate.print_("\nInitial Estimate:\n") # print
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# 4. Optimize the initial values using a Gauss-Newton nonlinear optimizer
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# The optimizer accepts an optional set of configuration parameters,
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# controlling things like convergence criteria, the type of linear
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# system solver to use, and the amount of information displayed during
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# optimization. We will set a few parameters as a demonstration.
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parameters = gtsam.GaussNewtonParams()
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# Stop iterating once the change in error between steps is less than this value
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parameters.setRelativeErrorTol(1e-5)
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# Do not perform more than N iteration steps
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parameters.setMaxIterations(100)
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# Create the optimizer ...
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optimizer = gtsam.GaussNewtonOptimizer(graph, initial_estimate, parameters)
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# ... and optimize
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result = optimizer.optimize()
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result.print_("Final Result:\n")
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# 5. Calculate and print marginal covariances for all variables
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marginals = gtsam.Marginals(graph, result)
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for i in range(1, 6):
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print("X{} covariance:\n{}\n".format(i, marginals.marginalCovariance(i)))
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@ -0,0 +1,4 @@
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These examples are almost identical to the old handwritten python wrapper
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examples. However, there are just some slight name changes, for example .print
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becomes .print_, and noiseModel.Diagonal becomes noiseModel_Diagonal etc...
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Also, annoyingly, instead of gtsam.Symbol('b',0) we now need to say gtsam.symbol(ord('b'), 0))
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@ -0,0 +1,38 @@
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"""
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A structure-from-motion example with landmarks
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- The landmarks form a 10 meter cube
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- The robot rotates around the landmarks, always facing towards the cube
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"""
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# pylint: disable=invalid-name, E1101
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import numpy as np
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import gtsam
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def createPoints():
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# Create the set of ground-truth landmarks
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points = [gtsam.Point3(10.0, 10.0, 10.0),
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gtsam.Point3(-10.0, 10.0, 10.0),
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gtsam.Point3(-10.0, -10.0, 10.0),
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gtsam.Point3(10.0, -10.0, 10.0),
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gtsam.Point3(10.0, 10.0, -10.0),
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gtsam.Point3(-10.0, 10.0, -10.0),
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gtsam.Point3(-10.0, -10.0, -10.0),
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gtsam.Point3(10.0, -10.0, -10.0)]
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return points
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def createPoses(K):
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# Create the set of ground-truth poses
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radius = 30.0
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angles = np.linspace(0, 2*np.pi, 8, endpoint=False)
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up = gtsam.Point3(0, 0, 1)
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target = gtsam.Point3(0, 0, 0)
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poses = []
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for theta in angles:
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position = gtsam.Point3(radius*np.cos(theta),
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radius*np.sin(theta), 0.0)
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camera = gtsam.PinholeCameraCal3_S2.Lookat(position, target, up, K)
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poses.append(camera.pose())
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return poses
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@ -0,0 +1,163 @@
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"""
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GTSAM Copyright 2010-2018, Georgia Tech Research Corporation,
|
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Atlanta, Georgia 30332-0415
|
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All Rights Reserved
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Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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See LICENSE for the license information
|
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An example of running visual SLAM using iSAM2.
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Author: Duy-Nguyen Ta (C++), Frank Dellaert (Python)
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"""
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# pylint: disable=invalid-name, E1101
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from __future__ import print_function
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import matplotlib.pyplot as plt
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import numpy as np
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from mpl_toolkits.mplot3d import Axes3D # pylint: disable=W0611
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import gtsam
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import gtsam.utils.plot as gtsam_plot
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from gtsam.examples import SFMdata
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def X(i):
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"""Create key for pose i."""
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return int(gtsam.symbol(ord('x'), i))
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def L(j):
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"""Create key for landmark j."""
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return int(gtsam.symbol(ord('l'), j))
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def visual_ISAM2_plot(result):
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"""
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VisualISAMPlot plots current state of ISAM2 object
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Author: Ellon Paiva
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Based on MATLAB version by: Duy Nguyen Ta and Frank Dellaert
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"""
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# Declare an id for the figure
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fignum = 0
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fig = plt.figure(fignum)
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axes = fig.gca(projection='3d')
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plt.cla()
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# Plot points
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# Can't use data because current frame might not see all points
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# marginals = Marginals(isam.getFactorsUnsafe(), isam.calculateEstimate())
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# gtsam.plot_3d_points(result, [], marginals)
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gtsam_plot.plot_3d_points(fignum, result, 'rx')
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# Plot cameras
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i = 0
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while result.exists(X(i)):
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pose_i = result.atPose3(X(i))
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gtsam_plot.plot_pose3(fignum, pose_i, 10)
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i += 1
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# draw
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axes.set_xlim3d(-40, 40)
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axes.set_ylim3d(-40, 40)
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axes.set_zlim3d(-40, 40)
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plt.pause(1)
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def visual_ISAM2_example():
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plt.ion()
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# Define the camera calibration parameters
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K = gtsam.Cal3_S2(50.0, 50.0, 0.0, 50.0, 50.0)
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# Define the camera observation noise model
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measurement_noise = gtsam.noiseModel_Isotropic.Sigma(
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2, 1.0) # one pixel in u and v
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# Create the set of ground-truth landmarks
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points = SFMdata.createPoints()
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# Create the set of ground-truth poses
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poses = SFMdata.createPoses(K)
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# Create an iSAM2 object. Unlike iSAM1, which performs periodic batch steps
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# to maintain proper linearization and efficient variable ordering, iSAM2
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# performs partial relinearization/reordering at each step. A parameter
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# structure is available that allows the user to set various properties, such
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# as the relinearization threshold and type of linear solver. For this
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# example, we we set the relinearization threshold small so the iSAM2 result
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# will approach the batch result.
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parameters = gtsam.ISAM2Params()
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parameters.setRelinearizeThreshold(0.01)
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parameters.setRelinearizeSkip(1)
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isam = gtsam.ISAM2(parameters)
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# Create a Factor Graph and Values to hold the new data
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graph = gtsam.NonlinearFactorGraph()
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initial_estimate = gtsam.Values()
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# Loop over the different poses, adding the observations to iSAM incrementally
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for i, pose in enumerate(poses):
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# Add factors for each landmark observation
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for j, point in enumerate(points):
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camera = gtsam.PinholeCameraCal3_S2(pose, K)
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measurement = camera.project(point)
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graph.push_back(gtsam.GenericProjectionFactorCal3_S2(
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measurement, measurement_noise, X(i), L(j), K))
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# Add an initial guess for the current pose
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# Intentionally initialize the variables off from the ground truth
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initial_estimate.insert(X(i), pose.compose(gtsam.Pose3(
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gtsam.Rot3.Rodrigues(-0.1, 0.2, 0.25), gtsam.Point3(0.05, -0.10, 0.20))))
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|
||||
# 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()
|
||||
|
|
@ -0,0 +1 @@
|
|||
from . import SFMdata
|
||||
|
|
@ -0,0 +1,76 @@
|
|||
"""Various plotting utlities."""
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
|
||||
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
|
||||
# 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)
|
||||
|
|
@ -306,10 +306,17 @@ public:
|
|||
: cols),
|
||||
stride
|
||||
) {}
|
||||
|
||||
MapBase &operator=(const MatrixType &other) {
|
||||
Base::operator=(other);
|
||||
return *this;
|
||||
}
|
||||
|
||||
virtual ~MapBase() { }
|
||||
};
|
||||
|
||||
|
||||
template <template<class,int,int,int,int,int> class DenseBase,
|
||||
template <template<class,int,int,int,int,int> class EigencyDenseBase,
|
||||
typename Scalar,
|
||||
int _Rows, int _Cols,
|
||||
int _Options = Eigen::AutoAlign |
|
||||
|
|
@ -341,16 +348,18 @@ template <template<class,int,int,int,int,int> class DenseBase,
|
|||
int _StrideOuter=0, int _StrideInner=0,
|
||||
int _MaxRows = _Rows,
|
||||
int _MaxCols = _Cols>
|
||||
class FlattenedMap: public MapBase<DenseBase<Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>, _MapOptions, Eigen::Stride<_StrideOuter, _StrideInner> > {
|
||||
class FlattenedMap: public MapBase<EigencyDenseBase<Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>, _MapOptions, Eigen::Stride<_StrideOuter, _StrideInner> > {
|
||||
public:
|
||||
typedef MapBase<DenseBase<Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>, _MapOptions, Eigen::Stride<_StrideOuter, _StrideInner> > Base;
|
||||
typedef MapBase<EigencyDenseBase<Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>, _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) {
|
||||
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<Scalar*>(other.data()),
|
||||
other.rows(),
|
||||
other.cols(),
|
||||
other.outerStride(),
|
||||
other.innerStride());
|
||||
}
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
|
|
@ -382,9 +399,16 @@ public:
|
|||
return static_cast<Base&>(*this);
|
||||
}
|
||||
|
||||
operator DenseBase<Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>() const {
|
||||
return DenseBase<Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>(static_cast<Base>(*this));
|
||||
operator EigencyDenseBase<Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>() const {
|
||||
return EigencyDenseBase<Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>(static_cast<Base>(*this));
|
||||
}
|
||||
|
||||
virtual ~FlattenedMap() {
|
||||
Py_XDECREF(object_);
|
||||
}
|
||||
|
||||
private:
|
||||
PyArrayObject * const object_;
|
||||
};
|
||||
|
||||
|
||||
|
|
@ -394,39 +418,60 @@ public:
|
|||
typedef MapBase<MatrixType> 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) {
|
||||
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<Scalar*>(other.data()),
|
||||
other.rows(),
|
||||
other.cols());
|
||||
}
|
||||
|
||||
return *this;
|
||||
}
|
||||
|
||||
Map &operator=(const MatrixType &other) {
|
||||
MapBase<MatrixType>::operator=(other);
|
||||
return *this;
|
||||
}
|
||||
|
||||
|
|
@ -441,6 +486,13 @@ public:
|
|||
operator MatrixType() const {
|
||||
return MatrixType(static_cast<Base>(*this));
|
||||
}
|
||||
|
||||
virtual ~Map() {
|
||||
Py_XDECREF(object_);
|
||||
}
|
||||
|
||||
private:
|
||||
PyArrayObject * const object_;
|
||||
};
|
||||
|
||||
|
||||
|
|
|
|||
41
gtsam.h
41
gtsam.h
|
|
@ -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;
|
||||
|
|
@ -2502,6 +2504,11 @@ class NavState {
|
|||
#include <gtsam/navigation/PreintegratedRotation.h>
|
||||
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<Vector3> getOmegaCoriolis() const;
|
||||
// boost::optional<Vector> getOmegaCoriolis() const;
|
||||
// boost::optional<Pose3> getBodyPSensor() const;
|
||||
};
|
||||
|
||||
#include <gtsam/navigation/PreintegrationParams.h>
|
||||
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 <gtsam/navigation/ImuFactor.h>
|
||||
|
|
@ -2660,6 +2676,27 @@ virtual class Pose3AttitudeFactor : gtsam::NonlinearFactor{
|
|||
gtsam::Unit3 bRef() const;
|
||||
};
|
||||
|
||||
#include <gtsam/navigation/Scenario.h>
|
||||
virtual class Scenario {};
|
||||
|
||||
virtual class ConstantTwistScenario : gtsam::Scenario {
|
||||
ConstantTwistScenario(const Vector& w, const Vector& v);
|
||||
gtsam::Pose3 pose(double t) const;
|
||||
Vector omega_b(double t) const;
|
||||
Vector velocity_n(double t) const;
|
||||
Vector acceleration_n(double t) const;
|
||||
};
|
||||
|
||||
virtual class AcceleratingScenario : gtsam::Scenario {
|
||||
AcceleratingScenario(const gtsam::Rot3& nRb, const gtsam::Point3& p0,
|
||||
const Vector& v0, const Vector& a_n,
|
||||
const Vector& omega_b);
|
||||
gtsam::Pose3 pose(double t) const;
|
||||
Vector omega_b(double t) const;
|
||||
Vector velocity_n(double t) const;
|
||||
Vector acceleration_n(double t) const;
|
||||
};
|
||||
|
||||
//*************************************************************************
|
||||
// Utilities
|
||||
//*************************************************************************
|
||||
|
|
|
|||
Loading…
Reference in New Issue