Example: maps_ransac_data_association
![maps_ransac_data_association screenshot](_images/maps_ransac_data_association_screenshot.png)
![maps_ransac_data_association screenshot](_images/maps_ransac_data_association_screenshot.png)
![maps_ransac_data_association screenshot](_images/maps_ransac_data_association_screenshot.png)
![maps_ransac_data_association screenshot](_images/maps_ransac_data_association_screenshot.png)
C++ example source code:
/* +------------------------------------------------------------------------+ | Mobile Robot Programming Toolkit (MRPT) | | https://www.mrpt.org/ | | | | Copyright (c) 2005-2024, Individual contributors, see AUTHORS file | | See: https://www.mrpt.org/Authors - All rights reserved. | | Released under BSD License. See: https://www.mrpt.org/License | +------------------------------------------------------------------------+ */ #include <mrpt/gui/CDisplayWindow3D.h> #include <mrpt/maps/CSimplePointsMap.h> #include <mrpt/math/geometry.h> #include <mrpt/opengl/CPointCloud.h> #include <mrpt/opengl/CSetOfLines.h> #include <mrpt/opengl/stock_objects.h> #include <mrpt/poses/CPose2D.h> #include <mrpt/poses/CPosePDFGaussian.h> #include <mrpt/poses/CPosePDFSOG.h> #include <mrpt/random.h> #include <mrpt/system/CTicTac.h> #include <mrpt/system/CTimeLogger.h> #include <mrpt/tfest/se2.h> #include <iostream> // Method explained in paper: // J.L. Blanco, J. Gonzalez-Jimenez, J.A. Fernandez-Madrigal, // "A Robust, Multi-Hypothesis Approach to Matching Occupancy Grid Maps", // Robotica, 2013. // http://dx.doi.org/10.1017/S0263574712000732 // ============= PARAMETERS =================== const size_t NUM_OBSERVATIONS_TO_SIMUL = 10; const size_t RANSAC_MINIMUM_INLIERS = 9; // Min. # of inliers to accept #define LOAD_MAP_FROM_FILE 0 // 1: load from "sMAP_FILE", 0: random map. #define SHOW_POINT_LABELS 0 const float normalizationStd = 0.15f; // 1 sigma noise (meters) const float ransac_mahalanobisDistanceThreshold = 5.0f; const size_t MINIMUM_RANSAC_ITERS = 100000; #if !LOAD_MAP_FROM_FILE const size_t NUM_MAP_FEATS = 100; const double MAP_SIZE_X = 50; const double MAP_SIZE_Y = 25; #else // Expected format of the 2D map is, for each line (one per landmark): // ID X Y const std::string sMAP_FILE = string("./DLRMap.txt"); #endif // ============================================== using namespace mrpt; using namespace mrpt::math; using namespace mrpt::random; using namespace mrpt::maps; using namespace mrpt::tfest; using namespace mrpt::img; using namespace std; struct TObs { size_t ID; // Ground truth ID double x, y; }; // ------------------------------------------------------ // TestRANSAC // ------------------------------------------------------ void TestRANSAC() { mrpt::gui::CDisplayWindow3D win( "MRPT example: ransac-data-association", 800, 600); mrpt::system::CTimeLogger timelog; // For dumping stats at the end mrpt::system::CTicTac timer; getRandomGenerator().randomize(); // randomize with time // -------------------------------- // Load feature map: // -------------------------------- CSimplePointsMap the_map; #if LOAD_MAP_FROM_FILE { CMatrixDouble M; M.loadFromTextFile(sMAP_FILE); // Launch except. on error ASSERT_(M.cols() == 3 && M.rows() > 2) const size_t nPts = M.rows(); the_map.resize(nPts); for (size_t i = 0; i < nPts; i++) the_map.setPoint(i, M(i, 1), M(i, 2)); } #else // Generate random MAP: the_map.resize(NUM_MAP_FEATS); for (size_t i = 0; i < NUM_MAP_FEATS; i++) { the_map.setPoint( i, getRandomGenerator().drawUniform(0, MAP_SIZE_X), getRandomGenerator().drawUniform(0, MAP_SIZE_Y)); } #endif const size_t nMapPts = the_map.size(); cout << "Loaded/generated map with " << nMapPts << " landmarks.\n"; const size_t nObs = NUM_OBSERVATIONS_TO_SIMUL; mrpt::opengl::CPointCloud::Ptr gl_obs_map = mrpt::opengl::CPointCloud::Create(); mrpt::opengl::CPointCloud::Ptr gl_result = mrpt::opengl::CPointCloud::Create(); mrpt::opengl::CSetOfObjects::Ptr gl_obs = mrpt::opengl::CSetOfObjects::Create(); mrpt::opengl::CSetOfObjects::Ptr gl_obs_txts = mrpt::opengl::CSetOfObjects::Create(); mrpt::opengl::CSetOfLines::Ptr gl_lines = mrpt::opengl::CSetOfLines::Create(); { mrpt::opengl::Scene::Ptr& scene = win.get3DSceneAndLock(); scene->getViewport("main")->setCustomBackgroundColor( TColorf(0.8f, 0.8f, 0.8f)); win.setCameraPointingToPoint(MAP_SIZE_X * 0.5, MAP_SIZE_Y * 0.5, 0); win.setCameraZoom(2 * MAP_SIZE_X); // scene->insert(mrpt::opengl::stock_objects::CornerXYZ()); // mrpt::opengl::CPointCloud::Ptr gl_map = mrpt::opengl::CPointCloud::Create(); gl_map->loadFromPointsMap(&the_map); gl_map->setColor(0, 0, 1); gl_map->setPointSize(3); scene->insert(gl_map); #if SHOW_POINT_LABELS for (size_t i = 0; i < the_map.size(); i++) { mrpt::opengl::CText::Ptr gl_txt = mrpt::opengl::CText::Create( mrpt::format("%u", static_cast<unsigned int>(i))); double x, y; the_map.getPoint(i, x, y); gl_txt->setLocation(x + 0.05, y + 0.05, 0.01); scene->insert(gl_txt); } #endif // scene->insert(gl_lines); // gl_obs_map->setColor(1, 0, 0); gl_obs_map->setPointSize(5); gl_result->setColor(0, 1, 0); gl_result->setPointSize(4); // gl_obs->insert(mrpt::opengl::stock_objects::CornerXYZ(0.6f)); gl_obs->insert(gl_obs_map); gl_obs->insert(gl_obs_txts); scene->insert(gl_obs); scene->insert(gl_result); win.unlockAccess3DScene(); win.repaint(); } // Repeat for each set of observations in the input file while (win.isOpen()) { // Read the observations themselves: vector<TObs> observations; observations.resize(nObs); const mrpt::poses::CPose2D GT_pose( mrpt::random::getRandomGenerator().drawUniform( -10, 10 + MAP_SIZE_X), mrpt::random::getRandomGenerator().drawUniform( -10, 10 + MAP_SIZE_Y), mrpt::random::getRandomGenerator().drawUniform(-M_PI, M_PI)); const mrpt::poses::CPose2D GT_pose_inv = -GT_pose; std::vector<nanoflann::ResultItem<size_t, float>> idxs; the_map.kdTreeRadiusSearch2D(GT_pose.x(), GT_pose.y(), 1000, idxs); ASSERT_(idxs.size() >= nObs); for (size_t i = 0; i < nObs; i++) { double gx, gy; the_map.getPoint(idxs[i].first, gx, gy); double lx, ly; GT_pose_inv.composePoint(gx, gy, lx, ly); observations[i].ID = idxs[i].first; observations[i].x = lx + mrpt::random::getRandomGenerator().drawGaussian1D( 0, normalizationStd); observations[i].y = ly + mrpt::random::getRandomGenerator().drawGaussian1D( 0, normalizationStd); } // ---------------------------------------------------- // Generate list of individual-compatible pairings // ---------------------------------------------------- TMatchingPairList all_correspondences; all_correspondences.reserve(nMapPts * nObs); // ALL possibilities: for (size_t j = 0; j < nObs; j++) { TMatchingPair match; match.localIdx = j; match.local.x = observations[j].x; match.local.y = observations[j].y; for (size_t i = 0; i < nMapPts; i++) { match.globalIdx = i; the_map.getPoint(i, match.global.x, match.global.y); all_correspondences.push_back(match); } } cout << "Generated " << all_correspondences.size() << " potential pairings.\n"; // ---------------------------------------------------- // Run RANSAC-based D-A // ---------------------------------------------------- timelog.enter("robustRigidTransformation"); timer.Tic(); mrpt::tfest::TSE2RobustParams params; mrpt::tfest::TSE2RobustResult results; params.ransac_minSetSize = RANSAC_MINIMUM_INLIERS; // ransac_minSetSize (to add the solution // to the SOG) params.ransac_maxSetSize = all_correspondences .size(); // ransac_maxSetSize: Test with all data points params.ransac_mahalanobisDistanceThreshold = ransac_mahalanobisDistanceThreshold; params.ransac_nSimulations = 0; // 0=auto params.ransac_fuseByCorrsMatch = true; params.ransac_fuseMaxDiffXY = 0.01f; params.ransac_fuseMaxDiffPhi = 0.1_deg; params.ransac_algorithmForLandmarks = true; params.probability_find_good_model = 0.999999; params.ransac_min_nSimulations = MINIMUM_RANSAC_ITERS; // (a lower limit to the auto-detected value // of ransac_nSimulations) params.verbose = true; // Run ransac data-association: mrpt::tfest::se2_l2_robust( all_correspondences, normalizationStd, params, results); timelog.leave("robustRigidTransformation"); mrpt::poses::CPosePDFSOG& best_poses = results.transformation; TMatchingPairList& out_best_pairings = results.largestSubSet; const double tim = timer.Tac(); cout << "RANSAC time: " << mrpt::system::formatTimeInterval(tim) << endl; cout << "# of SOG modes: " << best_poses.size() << endl; cout << "Best match has " << out_best_pairings.size() << " features:\n"; for (size_t i = 0; i < out_best_pairings.size(); i++) cout << out_best_pairings[i].globalIdx << " <-> " << out_best_pairings[i].localIdx << endl; cout << endl; // Generate "association vector": vector<int> obs2map_pairings(nObs, -1); for (size_t i = 0; i < out_best_pairings.size(); i++) obs2map_pairings[out_best_pairings[i].localIdx] = out_best_pairings[i].globalIdx == ((unsigned int)-1) ? -1 : out_best_pairings[i].globalIdx; cout << "1\n"; for (size_t i = 0; i < nObs; i++) cout << obs2map_pairings[i] << " "; cout << endl; gl_result->clear(); // Reconstruct the SE(2) transformation for these pairings: mrpt::poses::CPosePDFGaussian solution_pose; mrpt::tfest::se2_l2(out_best_pairings, solution_pose); // Normalized covariance: scale! solution_pose.cov *= square(normalizationStd); cout << "Solution pose: " << solution_pose.mean << endl; cout << "Ground truth pose: " << GT_pose << endl; { // mrpt::opengl::Scene::Ptr &scene = win.get3DSceneAndLock(); win.addTextMessage( 5, 5, "Blue: map landmarks | Red: Observations | White lines: Found " "correspondences", 0); // gl_obs_map->clear(); for (size_t k = 0; k < nObs; k++) gl_obs_map->insertPoint( observations[k].x, observations[k].y, 0.0); gl_obs->setPose(solution_pose.mean); #if SHOW_POINT_LABELS gl_obs_txts->clear(); for (size_t i = 0; i < nObs; i++) { mrpt::opengl::CText::Ptr gl_txt = mrpt::opengl::CText::Create( mrpt::format("%u", static_cast<unsigned int>(i))); const double x = observations[i].x; const double y = observations[i].y; gl_txt->setLocation(x + 0.05, y + 0.05, 0.01); gl_obs_txts->insert(gl_txt); } #endif // gl_lines->clear(); double sqerr = 0; size_t nPairs = 0; for (size_t k = 0; k < nObs; k++) { int map_idx = obs2map_pairings[k]; if (map_idx < 0) continue; nPairs++; double map_x, map_y; the_map.getPoint(map_idx, map_x, map_y); double obs_x, obs_y; solution_pose.mean.composePoint( observations[k].x, observations[k].y, obs_x, obs_y); const double z = 0; gl_lines->appendLine(map_x, map_y, 0, obs_x, obs_y, z); sqerr += mrpt::math::distanceSqrBetweenPoints<double>( map_x, map_y, obs_x, obs_y); } win.addTextMessage( 5, 20, "Ground truth pose : " + GT_pose.asString(), 1); win.addTextMessage( 5, 35, "RANSAC estimated pose: " + solution_pose.mean.asString() + mrpt::format(" | RMSE=%f", (nPairs ? sqerr / nPairs : 0.0)), 2); win.unlockAccess3DScene(); win.repaint(); cout << "nPairings: " << nPairs << " RMSE = " << (nPairs ? sqerr / nPairs : 0.0) << endl; win.waitForKey(); } } // end of for each set of observations } // ------------------------------------------------------ // MAIN // ------------------------------------------------------ int main() { try { TestRANSAC(); return 0; } catch (const std::exception& e) { std::cerr << "MRPT error: " << mrpt::exception_to_str(e) << std::endl; return -1; } }