367 lines
13 KiB
C++
367 lines
13 KiB
C++
/* ----------------------------------------------------------------------------
|
|
* GTSAM Copyright 2010, 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
|
|
* -------------------------------------------------------------------------- */
|
|
|
|
/**
|
|
* @file testSmartStereoFactor_iSAM2.cpp
|
|
* @brief Unit tests for ProjectionFactor Class
|
|
* @author Jose Luis Blanco-Claraco
|
|
* @date May 2019
|
|
*
|
|
* @note Originally based on ISAM2_SmartFactorStereo.cpp by Nghia Ho
|
|
*/
|
|
|
|
#include <CppUnitLite/TestHarness.h>
|
|
|
|
#include <gtsam/base/debug.h>
|
|
#include <gtsam/nonlinear/ISAM2.h>
|
|
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
|
|
#include <gtsam_unstable/slam/SmartStereoProjectionPoseFactor.h>
|
|
|
|
#include <array>
|
|
#include <fstream>
|
|
#include <iostream>
|
|
#include <sstream>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
// Set to 1 to enable verbose output of intermediary results
|
|
#define TEST_VERBOSE_OUTPUT 0
|
|
|
|
#if TEST_VERBOSE_OUTPUT
|
|
#define TEST_COUT(ARGS_) std::cout << ARGS_
|
|
#else
|
|
#define TEST_COUT(ARGS_) void(0)
|
|
#endif
|
|
|
|
// Tolerance for ground-truth pose comparison:
|
|
static const double tol = 1e-3;
|
|
|
|
// Synthetic dataset generated with rwt
|
|
// (https://github.com/jlblancoc/recursive-world-toolkit)
|
|
// Camera parameters
|
|
const double fx = 200.0;
|
|
const double fy = 150.0;
|
|
const double cx = 512.0;
|
|
const double cy = 384.0;
|
|
const double baseline = 0.2; // meters
|
|
|
|
using timestep_t = std::size_t;
|
|
using lm_id_t = int;
|
|
|
|
struct stereo_meas_t {
|
|
stereo_meas_t(lm_id_t id, double lu, double ru, double v_lr)
|
|
: lm_id{id}, left_u{lu}, right_u{ru}, v{v_lr} {}
|
|
|
|
lm_id_t lm_id{-1}; // landmark id
|
|
double left_u{0}, right_u{0}, v{0};
|
|
};
|
|
|
|
static std::map<timestep_t, std::vector<stereo_meas_t>> dataset = {
|
|
{0,
|
|
{{0, 911.99993896, 712.00000000, 384.0},
|
|
{159, 311.99996948, 211.99996948, 384.0},
|
|
{3, 378.66665649, 312.00000000, 384.0},
|
|
{2, 645.33331299, 578.66662598, 384.0},
|
|
{157, 111.99994659, 11.99993896, 384.0},
|
|
{4, 578.66662598, 545.33331299, 384.0},
|
|
{5, 445.33331299, 412.00000000, 384.0},
|
|
{6, 562.00000000, 537.00000000, 384.0}}},
|
|
{1,
|
|
{{0, 1022.06353760, 762.57519531, 384.0},
|
|
{159, 288.30487061, 177.80273438, 384.0},
|
|
{2, 655.30645752, 583.12127686, 384.0},
|
|
{3, 368.60937500, 297.43176270, 384.0},
|
|
{4, 581.82666016, 547.16766357, 384.0},
|
|
{5, 443.66183472, 409.23681641, 384.0},
|
|
{6, 564.35980225, 538.62115479, 384.0},
|
|
{7, 461.66418457, 436.05477905, 384.0},
|
|
{8, 550.32220459, 531.75256348, 384.0},
|
|
{9, 476.17767334, 457.67541504, 384.0}}},
|
|
{2,
|
|
{{159, 257.97128296, 134.26287842, 384.0},
|
|
{2, 666.87255859, 588.07275391, 384.0},
|
|
{3, 356.53823853, 280.10061646, 384.0},
|
|
{4, 585.10949707, 548.99212646, 384.0},
|
|
{5, 441.66403198, 406.05108643, 384.0},
|
|
{6, 566.75402832, 540.21868896, 384.0},
|
|
{7, 461.16207886, 434.90002441, 384.0},
|
|
{8, 552.28387451, 533.30230713, 384.0},
|
|
{9, 476.63549805, 457.79418945, 384.0},
|
|
{10, 546.48394775, 530.53009033, 384.0}}},
|
|
{3,
|
|
{{159, 218.10592651, 77.30914307, 384.0},
|
|
{2, 680.54644775, 593.68103027, 384.0},
|
|
{3, 341.92507935, 259.28231812, 384.0},
|
|
{4, 588.53289795, 550.80499268, 384.0},
|
|
{5, 439.29989624, 402.39105225, 384.0},
|
|
{6, 569.18627930, 541.78991699, 384.0},
|
|
{7, 460.47863770, 433.51678467, 384.0},
|
|
{8, 554.24902344, 534.82952881, 384.0},
|
|
{9, 477.00451660, 457.80438232, 384.0},
|
|
{10, 548.33770752, 532.07501221, 384.0},
|
|
{11, 483.58688354, 467.47830200, 384.0},
|
|
{12, 542.36785889, 529.29321289, 384.0}}},
|
|
{4,
|
|
{{2, 697.09454346, 600.18432617, 384.0},
|
|
{3, 324.03643799, 233.97094727, 384.0},
|
|
{4, 592.11877441, 552.60449219, 384.0},
|
|
{5, 436.52197266, 398.19531250, 384.0},
|
|
{6, 571.66101074, 543.33209229, 384.0},
|
|
{7, 459.59658813, 431.88333130, 384.0},
|
|
{8, 556.21801758, 536.33258057, 384.0},
|
|
{9, 477.27893066, 457.69882202, 384.0},
|
|
{10, 550.18920898, 533.60003662, 384.0},
|
|
{11, 484.24472046, 467.86862183, 384.0},
|
|
{12, 544.14727783, 530.86157227, 384.0},
|
|
{13, 491.26141357, 478.11267090, 384.0},
|
|
{14, 541.29949951, 529.57086182, 384.0},
|
|
{15, 494.58111572, 482.95935059, 384.0}}}};
|
|
|
|
// clang-format off
|
|
/*
|
|
% Ground truth path of the SENSOR, and the ROBOT
|
|
% STEP X Y Z QR QX QY QZ | X Y Z QR QX QY QZ
|
|
----------------------------------------------------------------------------------------------------------------------------------------
|
|
0 0.000000 0.000000 0.000000 0.500000 -0.500000 0.500000 -0.500000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000
|
|
1 0.042019 -0.008403 0.000000 0.502446 -0.502446 0.497542 -0.497542 0.042019 -0.008403 0.000000 0.999988 0.000000 0.000000 0.004905
|
|
2 0.084783 -0.016953 0.000000 0.504879 -0.504879 0.495073 -0.495073 0.084783 -0.016953 0.000000 0.999952 0.000000 0.000000 0.009806
|
|
3 0.128305 -0.025648 0.000000 0.507299 -0.507299 0.492592 -0.492592 0.128305 -0.025648 0.000000 0.999892 0.000000 0.000000 0.014707
|
|
4 0.172605 -0.034490 0.000000 0.509709 -0.509709 0.490098 -0.490098 0.172605 -0.034490 0.000000 0.999808 0.000000 0.000000 0.019611
|
|
*/
|
|
// clang-format on
|
|
|
|
// Ground truth using camera pose = vehicle frame
|
|
// The table above uses:
|
|
// camera +x = vehicle -y
|
|
// camera +y = vehicle -z
|
|
// camera +z = vehicle +x
|
|
static const std::map<timestep_t, gtsam::Point3> gt_positions = {
|
|
{0, {0.000000, 0.000000, 0.0}},
|
|
{1, {0.042019, -0.008403, 0.0}},
|
|
{2, {0.084783, -0.016953, 0.0}},
|
|
{3, {0.128305, -0.025648, 0.0}},
|
|
{4, {0.172605, -0.034490, 0.0}}};
|
|
|
|
// Batch version, to compare against iSAM2 solution.
|
|
TEST(testISAM2SmartFactor, Stereo_Batch) {
|
|
TEST_COUT("============ Running: Batch ============\n");
|
|
|
|
using namespace gtsam;
|
|
using symbol_shorthand::V;
|
|
using symbol_shorthand::X;
|
|
|
|
const auto K =
|
|
boost::make_shared<Cal3_S2Stereo>(fx, fy, .0, cx, cy, baseline);
|
|
|
|
// Pose prior - at identity
|
|
auto priorPoseNoise = noiseModel::Diagonal::Sigmas(
|
|
(Vector(6) << Vector3::Constant(0.2), Vector3::Constant(0.2)).finished());
|
|
|
|
// Map: landmark_id => smart_factor_index inside iSAM2
|
|
std::map<lm_id_t, FactorIndex> lm2factor;
|
|
|
|
// Storage of smart factors:
|
|
std::map<lm_id_t, SmartStereoProjectionPoseFactor::shared_ptr> smartFactors;
|
|
|
|
NonlinearFactorGraph batch_graph;
|
|
Values batch_values;
|
|
|
|
// Run one timestep at once:
|
|
for (const auto &entries : dataset) {
|
|
// 1) Process new observations:
|
|
// ------------------------------
|
|
const auto kf_id = entries.first;
|
|
const std::vector<stereo_meas_t> &obs = entries.second;
|
|
|
|
for (const stereo_meas_t &stObs : obs) {
|
|
if (smartFactors.count(stObs.lm_id) == 0) {
|
|
auto noise = noiseModel::Isotropic::Sigma(3, 0.1);
|
|
SmartProjectionParams parm(HESSIAN, ZERO_ON_DEGENERACY);
|
|
|
|
smartFactors[stObs.lm_id] =
|
|
boost::make_shared<SmartStereoProjectionPoseFactor>(noise, parm);
|
|
|
|
batch_graph.push_back(smartFactors[stObs.lm_id]);
|
|
}
|
|
|
|
TEST_COUT("Adding stereo observation from KF #" << kf_id << " for LM #"
|
|
<< stObs.lm_id << "\n");
|
|
|
|
smartFactors[stObs.lm_id]->add(
|
|
StereoPoint2(stObs.left_u, stObs.right_u, stObs.v), X(kf_id), K);
|
|
}
|
|
|
|
// prior, for the first keyframe:
|
|
if (kf_id == 0) {
|
|
batch_graph.addPrior(X(kf_id), Pose3::Identity(), priorPoseNoise);
|
|
}
|
|
|
|
batch_values.insert(X(kf_id), Pose3::Identity());
|
|
}
|
|
|
|
LevenbergMarquardtParams parameters;
|
|
#if TEST_VERBOSE_OUTPUT
|
|
parameters.verbosity = NonlinearOptimizerParams::LINEAR;
|
|
parameters.verbosityLM = LevenbergMarquardtParams::TRYDELTA;
|
|
#endif
|
|
|
|
LevenbergMarquardtOptimizer lm(batch_graph, batch_values, parameters);
|
|
|
|
Values finalEstimate = lm.optimize();
|
|
#if TEST_VERBOSE_OUTPUT
|
|
finalEstimate.print("LevMarq estimate:");
|
|
#endif
|
|
|
|
// GT:
|
|
// camera +x = vehicle -y
|
|
// camera +y = vehicle -z
|
|
// camera +z = vehicle +x
|
|
for (const auto > : gt_positions) {
|
|
const Pose3 p = finalEstimate.at<Pose3>(X(gt.first));
|
|
EXPECT(assert_equal(p.x(), -gt.second.y(), tol));
|
|
EXPECT(assert_equal(p.y(), -gt.second.z(), tol));
|
|
EXPECT(assert_equal(p.z(), gt.second.x(), tol));
|
|
}
|
|
}
|
|
|
|
TEST(testISAM2SmartFactor, Stereo_iSAM2) {
|
|
TEST_COUT("======= Running: iSAM2 ==========\n");
|
|
|
|
#if TEST_VERBOSE_OUTPUT
|
|
SETDEBUG("ISAM2 update", true);
|
|
// SETDEBUG("ISAM2 update verbose",true);
|
|
#endif
|
|
|
|
using namespace gtsam;
|
|
using symbol_shorthand::V;
|
|
using symbol_shorthand::X;
|
|
|
|
const auto K =
|
|
boost::make_shared<Cal3_S2Stereo>(fx, fy, .0, cx, cy, baseline);
|
|
|
|
ISAM2Params parameters;
|
|
parameters.relinearizeThreshold = 0.01;
|
|
parameters.evaluateNonlinearError = true;
|
|
|
|
// Do not cache smart factors:
|
|
parameters.cacheLinearizedFactors = false;
|
|
|
|
// Important: must set relinearizeSkip=1 to additional calls to update() to
|
|
// have a real effect.
|
|
parameters.relinearizeSkip = 1;
|
|
|
|
ISAM2 isam(parameters);
|
|
|
|
// Pose prior - at identity
|
|
auto priorPoseNoise = noiseModel::Diagonal::Sigmas(
|
|
(Vector(6) << Vector3::Constant(0.2), Vector3::Constant(0.2)).finished());
|
|
|
|
// Map: landmark_id => smart_factor_index inside iSAM2
|
|
std::map<lm_id_t, FactorIndex> lm2factor;
|
|
|
|
// Storage of smart factors:
|
|
std::map<lm_id_t, SmartStereoProjectionPoseFactor::shared_ptr> smartFactors;
|
|
|
|
Pose3 lastKeyframePose = Pose3::Identity();
|
|
|
|
// Run one timestep at once:
|
|
for (const auto &entries : dataset) {
|
|
// 1) Process new observations:
|
|
// ------------------------------
|
|
const auto kf_id = entries.first;
|
|
const std::vector<stereo_meas_t> &obs = entries.second;
|
|
|
|
// Special instructions for using iSAM2 + smart factors:
|
|
// Must fill factorNewAffectedKeys:
|
|
FastMap<FactorIndex, KeySet> factorNewAffectedKeys;
|
|
NonlinearFactorGraph newFactors;
|
|
|
|
// Map: factor index in the internal graph of iSAM2 => landmark_id
|
|
std::map<FactorIndex, lm_id_t> newFactor2lm;
|
|
|
|
for (const stereo_meas_t &stObs : obs) {
|
|
if (smartFactors.count(stObs.lm_id) == 0) {
|
|
auto noise = noiseModel::Isotropic::Sigma(3, 0.1);
|
|
SmartProjectionParams params(HESSIAN, ZERO_ON_DEGENERACY);
|
|
|
|
smartFactors[stObs.lm_id] =
|
|
boost::make_shared<SmartStereoProjectionPoseFactor>(noise, params);
|
|
newFactor2lm[newFactors.size()] = stObs.lm_id;
|
|
newFactors.push_back(smartFactors[stObs.lm_id]);
|
|
} else {
|
|
// Only if the factor *already* existed:
|
|
factorNewAffectedKeys[lm2factor.at(stObs.lm_id)].insert(X(kf_id));
|
|
}
|
|
|
|
TEST_COUT("Adding stereo observation from KF #" << kf_id << " for LM #"
|
|
<< stObs.lm_id << "\n");
|
|
|
|
smartFactors[stObs.lm_id]->add(
|
|
StereoPoint2(stObs.left_u, stObs.right_u, stObs.v), X(kf_id), K);
|
|
}
|
|
|
|
// prior, for the first keyframe:
|
|
if (kf_id == 0) {
|
|
newFactors.addPrior(X(kf_id), Pose3::Identity(), priorPoseNoise);
|
|
}
|
|
|
|
// 2) Run iSAM2:
|
|
// ------------------------------
|
|
Values newValues;
|
|
newValues.insert(X(kf_id), lastKeyframePose);
|
|
|
|
TEST_COUT("Running iSAM2 for frame: " << kf_id << "\n");
|
|
|
|
ISAM2UpdateParams updateParams;
|
|
updateParams.newAffectedKeys = std::move(factorNewAffectedKeys);
|
|
|
|
ISAM2Result res = isam.update(newFactors, newValues, updateParams);
|
|
|
|
for (const auto &f2l : newFactor2lm)
|
|
lm2factor[f2l.second] = res.newFactorsIndices.at(f2l.first);
|
|
|
|
TEST_COUT("error before1: " << res.errorBefore.value() << "\n");
|
|
TEST_COUT("error after1 : " << res.errorAfter.value() << "\n");
|
|
|
|
// Additional refining steps:
|
|
ISAM2Result res2 = isam.update();
|
|
TEST_COUT("error before2: " << res2.errorBefore.value() << "\n");
|
|
TEST_COUT("error after2 : " << res2.errorAfter.value() << "\n");
|
|
|
|
Values currentEstimate = isam.calculateEstimate();
|
|
#if TEST_VERBOSE_OUTPUT
|
|
currentEstimate.print("currentEstimate:");
|
|
#endif
|
|
|
|
// Keep last KF pose as initial pose of the next one, to reduce the need
|
|
// to run more non-linear iterations:
|
|
lastKeyframePose = currentEstimate.at(X(kf_id)).cast<Pose3>();
|
|
|
|
} // end for each timestep
|
|
|
|
Values finalEstimate = isam.calculateEstimate();
|
|
|
|
// GT:
|
|
// camera +x = vehicle -y
|
|
// camera +y = vehicle -z
|
|
// camera +z = vehicle +x
|
|
for (const auto > : gt_positions) {
|
|
const Pose3 p = finalEstimate.at<Pose3>(X(gt.first));
|
|
EXPECT(assert_equal(p.x(), -gt.second.y(), tol));
|
|
EXPECT(assert_equal(p.y(), -gt.second.z(), tol));
|
|
EXPECT(assert_equal(p.z(), gt.second.x(), tol));
|
|
}
|
|
}
|
|
|
|
/* *************************************************************************
|
|
*/
|
|
int main() {
|
|
TestResult tr;
|
|
return TestRegistry::runAllTests(tr);
|
|
}
|