def __init__(self): super().__init__() # g2o::BlockSlover_6_3(g2o::BlockSolver_6_3::LinearSolverType*) linear_solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3()) solver = g2o.OptimizationAlgorithmLevenberg(linear_solver) super().set_algorithm(solver) # additional parameters # self._map = None # self.vertex_seq_generator = AtomicCounter() self.edge_seq_generator = AtomicCounter() # Point | Frame | Landmark -> Vertex mapping # inverse vertex query self._ivq = {} # (Vertex, Vetex) -> Edge mapping, a sparse matrix # inverse edges query self._ieq = {} # self.USE_LANDMARKS = False
def __init__(self, ): super().__init__() #solver = g2o.BlockSolverX(g2o.LinearSolverEigenX()) solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3()) #solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3()) #solver = g2o.BlockSolverSE3(g2o.LinearSolverPCGSE3()) #solver = g2o.BlockSolverSE3(g2o.LinearSolverDenseSE3()) #solver = g2o.OptimizationAlgorithmLevenberg(solver) solver = g2o.OptimizationAlgorithmGaussNewton(solver) super().set_algorithm(solver)
def __init__(self, ): super().__init__() # Higher confident (better than CHOLMOD, according to # paper "3-D Mapping With an RGB-D Camera") solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3()) solver = g2o.OptimizationAlgorithmLevenberg(solver) super().set_algorithm(solver) # Convergence Criterion terminate = g2o.SparseOptimizerTerminateAction() terminate.set_gain_threshold(1e-6) super().add_post_iteration_action(terminate) # Robust cost Function (Huber function) delta self.delta = np.sqrt(5.991) self.aborted = False
def prepare_optimiser(self, verbose): # create g2o optimizer self.opt = g2o.SparseOptimizer() self.solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3()) self.solver = g2o.OptimizationAlgorithmLevenberg(self.solver) self.opt.set_algorithm(self.solver) # add normalized camera cam = g2o.CameraParameters(1.0, (0.0, 0.0), 0) cam.set_id(0) self.opt.add_parameter(cam) self.robust_kernel = g2o.RobustKernelHuber(np.sqrt(5.991)) self.graph_frames, self.graph_points = {}, {} if verbose: self.opt.set_verbose(True)
def optimize(self, max_iters): opt = g2o.SparseOptimizer() solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3()) solver = g2o.OptimizationAlgorithmLevenberg(solver) opt.set_algorithm(solver) robust_kernel = g2o.RobustKernelHuber(np.sqrt(5.991)) # adding frames to graph for f in self.frames: sbacam = g2o.SBACam(g2o.SE3Quat(f.pose[0:3, 0:3], f.pose[0:3, 3])) sbacam.set_cam(f.T[0][0], f.T[1][1], f.T[2][0], f.T[2][1], 1.0) v_se3 = g2o.VertexCam() v_se3.set_id(f.id) v_se3.set_estimate(sbacam) v_se3.set_fixed(f.id == 0) opt.add_vertex(v_se3) # add points to graph for p in self.points: pt = g2o.VertexSBAPointXYZ() pt.set_id(p.id + 0x10000) pt.set_estimate(p.location[0:3]) pt.set_marginalized(True) pt.set_fixed(False) opt.add_vertex(pt) # add connections between frames that contain a point in the graph for f in p.frames: edge = g2o.EdgeProjectP2MC() edge.set_vertex(0, pt) edge.set_vertex(1, opt.vertex(f.id)) uv = f.kpts[f.pts.index(p)] edge.set_measurement(uv) edge.set_information(np.eye(2)) edge.set_robust_kernel(robust_kernel) opt.add_edge(edge) opt.set_verbose(True) opt.initialize_optimization() opt.optimize(max_iters)
def bundle_adjustment(points_3d, points_2d, r_mat, t_vec): optimizer = g2o.SparseOptimizer() solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3()) solver = g2o.OptimizationAlgorithmLevenberg(solver) optimizer.set_algorithm(solver) pose = g2o.VertexSE3Expmap() pose.set_estimate(g2o.SE3Quat(r_mat, t_vec.reshape((3, )))) pose.set_id(0) optimizer.add_vertex(pose) index = 1 for p_3d in points_3d: point = g2o.VertexSBAPointXYZ() point.set_id(index) point.set_estimate(p_3d) point.set_marginalized(True) optimizer.add_vertex(point) index += 1 camera = g2o.CameraParameters(K[0, 0], np.array([K[0, 2], K[1, 2]]), 0) camera.set_id(0) optimizer.add_parameter(camera) index = 1 for p_2d in points_2d: edge = g2o.EdgeProjectXYZ2UV() edge.set_id(index) edge.set_vertex(0, optimizer.vertex(index)) edge.set_vertex(1, pose) edge.set_measurement(p_2d) edge.set_parameter_id(0, 0) edge.set_information(np.identity(2)) optimizer.add_edge(edge) index += 1 optimizer.initialize_optimization() optimizer.set_verbose(True) optimizer.optimize(100) print('T = \n', pose.estimate().matrix())
def main(): optimizer = g2o.SparseOptimizer() solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3()) solver = g2o.OptimizationAlgorithmLevenberg(solver) optimizer.set_algorithm(solver) true_points = np.hstack([ np.random.random((500, 1)) * 3 - 1.5, np.random.random((500, 1)) - 0.5, np.random.random((500, 1)) + 3 ]) focal_length = [500, 500] principal_point = [320, 240] baseline = 0.075 camera = g2o.CameraParameters(500, [320, 240], 0.075) camera.set_id(10000) optimizer.add_parameter(camera) true_poses = [] num_pose = 5 for i in range(num_pose): # pose here transform points from world coordinates to camera coordinates pose = g2o.SE3Quat(np.identity(3), [i * 0.04 - 1, 0, 0]) true_poses.append(pose) v_se3 = g2o.VertexSE3Expmap() v_se3.set_id(i) v_se3.set_estimate(pose) if i < 2: v_se3.set_fixed(True) optimizer.add_vertex(v_se3) point_id = num_pose inliers = dict() sse = defaultdict(float) for i, point in enumerate(true_points): visible = [] for j, pose in enumerate(true_poses): z = camera.cam_map(pose * point) if 0 <= z[0] < 640 and 0 <= z[1] < 480: visible.append((j, z)) if len(visible) < 2: continue vp = g2o.VertexSBAPointXYZ() vp.set_id(point_id) vp.set_marginalized(True) vp.set_estimate(point + np.random.randn(3)) optimizer.add_vertex(vp) inlier = True for j, z in visible: if np.random.random() < args.outlier_ratio: inlier = False z = np.array([ np.random.uniform(64, 640), np.random.uniform(0, 480), np.random.uniform(0, 64) ]) # disparity z[2] = z[0] - z[2] z += np.random.randn(2) * args.pixel_noise edge = g2o.Edge_XYZ_VSC() edge.set_vertex(0, vp) edge.set_vertex(1, optimizer.vertex(j)) edge.set_measurement(z) edge.set_information(np.identity(2)) if args.robust_kernel: edge.set_robust_kernel(g2o.RobustKernelHuber()) edge.set_parameter_id(0, 0) optimizer.add_edge(edge) if inlier: inliers[point_id] = i error = vp.estimate() - true_points[i] sse[0] += np.sum(error**2) point_id += 1 print('Performing full BA:') optimizer.initialize_optimization() optimizer.set_verbose(True) optimizer.optimize(10) for i in inliers: vp = optimizer.vertex(i) error = vp.estimate() - true_points[inliers[i]] sse[1] += np.sum(error**2) print('\nCamera focal: \n{}\n'.format(camera.focal_length)) print('\nCamera principal point: \n{}\n'.format(camera.principal_point)) print('\nRMSE (inliers only):') print('before optimization:', np.sqrt(sse[0] / len(inliers))) print('after optimization:', np.sqrt(sse[1] / len(inliers)))
def __init__(self, ): super().__init__() solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3()) solver = g2o.OptimizationAlgorithmLevenberg(solver) super().set_algorithm(solver)
def bundle_adjustment(keyframes, points, local_window, fixed_points=False, verbose=False, rounds=10, use_robust_kernel=False, abort_flag=g2o.Flag()): if local_window is None: local_frames = keyframes else: local_frames = keyframes[-local_window:] # create g2o optimizer opt = g2o.SparseOptimizer() block_solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3()) #block_solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3()) solver = g2o.OptimizationAlgorithmLevenberg(block_solver) opt.set_algorithm(solver) opt.set_force_stop_flag(abort_flag) thHuberMono = math.sqrt(5.991); # chi-square 2 DOFS graph_keyframes, graph_points = {}, {} # add frame vertices to graph for kf in (local_frames if fixed_points else keyframes): # if points are fixed then consider just the local frames, otherwise we need all frames or at least two frames for each point if kf.is_bad: continue #print('adding vertex frame ', f.id, ' to graph') se3 = g2o.SE3Quat(kf.Rcw, kf.tcw) v_se3 = g2o.VertexSE3Expmap() v_se3.set_estimate(se3) v_se3.set_id(kf.kid * 2) # even ids (use f.kid here!) v_se3.set_fixed(kf.kid==0 or kf not in local_frames) #(use f.kid here!) opt.add_vertex(v_se3) # confirm pose correctness #est = v_se3.estimate() #assert np.allclose(pose[0:3, 0:3], est.rotation().matrix()) #assert np.allclose(pose[0:3, 3], est.translation()) graph_keyframes[kf] = v_se3 num_edges = 0 # add point vertices to graph for p in points: assert(p is not None) if p.is_bad: # do not consider bad points continue if __debug__: if not any([f in keyframes for f in p.keyframes()]): Printer.red('point without a viewing frame!!') continue #print('adding vertex point ', p.id,' to graph') v_p = g2o.VertexSBAPointXYZ() v_p.set_id(p.id * 2 + 1) # odd ids v_p.set_estimate(p.pt[0:3]) v_p.set_marginalized(True) v_p.set_fixed(fixed_points) opt.add_vertex(v_p) graph_points[p] = v_p # add edges for kf, idx in p.observations(): if kf.is_bad: continue if kf not in graph_keyframes: continue #print('adding edge between point ', p.id,' and frame ', f.id) edge = g2o.EdgeSE3ProjectXYZ() edge.set_vertex(0, v_p) edge.set_vertex(1, graph_keyframes[kf]) edge.set_measurement(kf.kpsu[idx]) invSigma2 = Frame.feature_manager.inv_level_sigmas2[kf.octaves[idx]] edge.set_information(np.eye(2)*invSigma2) if use_robust_kernel: edge.set_robust_kernel(g2o.RobustKernelHuber(thHuberMono)) edge.fx = kf.camera.fx edge.fy = kf.camera.fy edge.cx = kf.camera.cx edge.cy = kf.camera.cy opt.add_edge(edge) num_edges += 1 if verbose: opt.set_verbose(True) opt.initialize_optimization() opt.optimize(rounds) # put frames back for kf in graph_keyframes: est = graph_keyframes[kf].estimate() #R = est.rotation().matrix() #t = est.translation() #f.update_pose(poseRt(R, t)) kf.update_pose(g2o.Isometry3d(est.orientation(), est.position())) # put points back if not fixed_points: for p in graph_points: p.update_position(np.array(graph_points[p].estimate())) p.update_normal_and_depth(force=True) mean_squared_error = opt.active_chi2()/max(num_edges,1) return mean_squared_error
def local_bundle_adjustment(keyframes, points, keyframes_ref=[], fixed_points=False, verbose=False, rounds=10, abort_flag=g2o.Flag(), map_lock=None): # create g2o optimizer opt = g2o.SparseOptimizer() block_solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3()) #block_solver = g2o.BlockSolverSE3(g2o.LinearSolverEigenSE3()) #block_solver = g2o.BlockSolverSE3(g2o.LinearSolverCholmodSE3()) solver = g2o.OptimizationAlgorithmLevenberg(block_solver) opt.set_algorithm(solver) opt.set_force_stop_flag(abort_flag) #robust_kernel = g2o.RobustKernelHuber(np.sqrt(5.991)) # chi-square 2 DOFs thHuberMono = math.sqrt(5.991); # chi-square 2 DOFS graph_keyframes, graph_points = {}, {} # add frame vertices to graph for kf in keyframes: if kf.is_bad: continue #print('adding vertex frame ', f.id, ' to graph') se3 = g2o.SE3Quat(kf.Rcw, kf.tcw) v_se3 = g2o.VertexSE3Expmap() v_se3.set_estimate(se3) v_se3.set_id(kf.kid * 2) # even ids (use f.kid here!) v_se3.set_fixed(kf.kid==0) # (use f.kid here!) opt.add_vertex(v_se3) graph_keyframes[kf] = v_se3 # confirm pose correctness #est = v_se3.estimate() #assert np.allclose(pose[0:3, 0:3], est.rotation().matrix()) #assert np.allclose(pose[0:3, 3], est.translation()) # add reference frame vertices to graph for kf in keyframes_ref: if kf.is_bad: continue #print('adding vertex frame ', f.id, ' to graph') se3 = g2o.SE3Quat(kf.Rcw, kf.tcw) v_se3 = g2o.VertexSE3Expmap() v_se3.set_estimate(se3) v_se3.set_id(kf.kid * 2) # even ids (use f.kid here!) v_se3.set_fixed(True) opt.add_vertex(v_se3) graph_keyframes[kf] = v_se3 graph_edges = {} num_edges = 0 num_bad_edges = 0 # add point vertices to graph for p in points: assert(p is not None) if p.is_bad: # do not consider bad points continue if not any([f in keyframes for f in p.keyframes()]): Printer.orange('point %d without a viewing keyframe in input keyframes!!' %(p.id)) #Printer.orange(' keyframes: ',p.observations_string()) continue #print('adding vertex point ', p.id,' to graph') v_p = g2o.VertexSBAPointXYZ() v_p.set_id(p.id * 2 + 1) # odd ids v_p.set_estimate(p.pt[0:3]) v_p.set_marginalized(True) v_p.set_fixed(fixed_points) opt.add_vertex(v_p) graph_points[p] = v_p # add edges for kf, p_idx in p.observations(): if kf.is_bad: continue if kf not in graph_keyframes: continue if __debug__: p_f = kf.get_point_match(p_idx) if p_f != p: print('frame: ', kf.id, ' missing point ', p.id, ' at index p_idx: ', p_idx) if p_f is not None: print('p_f:', p_f) print('p:',p) assert(kf.get_point_match(p_idx) is p) #print('adding edge between point ', p.id,' and frame ', f.id) edge = g2o.EdgeSE3ProjectXYZ() edge.set_vertex(0, v_p) edge.set_vertex(1, graph_keyframes[kf]) edge.set_measurement(kf.kpsu[p_idx]) invSigma2 = Frame.feature_manager.inv_level_sigmas2[kf.octaves[p_idx]] edge.set_information(np.eye(2)*invSigma2) edge.set_robust_kernel(g2o.RobustKernelHuber(thHuberMono)) edge.fx = kf.camera.fx edge.fy = kf.camera.fy edge.cx = kf.camera.cx edge.cy = kf.camera.cy opt.add_edge(edge) graph_edges[edge] = (p,kf,p_idx) # one has kf.points[p_idx] == p num_edges += 1 if verbose: opt.set_verbose(True) if abort_flag.value: return -1,0 # initial optimization opt.initialize_optimization() opt.optimize(5) if not abort_flag.value: chi2Mono = 5.991 # chi-square 2 DOFs # check inliers observation for edge, edge_data in graph_edges.items(): p = edge_data[0] if p.is_bad: continue if edge.chi2() > chi2Mono or not edge.is_depth_positive(): edge.set_level(1) num_bad_edges += 1 edge.set_robust_kernel(None) # optimize again without outliers opt.initialize_optimization() opt.optimize(rounds) # search for final outlier observations and clean map num_bad_observations = 0 # final bad observations outliers_data = [] for edge, edge_data in graph_edges.items(): p, kf, p_idx = edge_data if p.is_bad: continue assert(kf.get_point_match(p_idx) is p) if edge.chi2() > chi2Mono or not edge.is_depth_positive(): num_bad_observations += 1 outliers_data.append(edge_data) if map_lock is None: map_lock = RLock() # put a fake lock with map_lock: # remove outlier observations for d in outliers_data: p, kf, p_idx = d p_f = kf.get_point_match(p_idx) if p_f is not None: assert(p_f is p) p.remove_observation(kf,p_idx) # the following instruction is now included in p.remove_observation() #f.remove_point(p) # it removes multiple point instances (if these are present) #f.remove_point_match(p_idx) # this does not remove multiple point instances, but now there cannot be multiple instances any more # put frames back for kf in graph_keyframes: est = graph_keyframes[kf].estimate() #R = est.rotation().matrix() #t = est.translation() #f.update_pose(poseRt(R, t)) kf.update_pose(g2o.Isometry3d(est.orientation(), est.position())) # put points back if not fixed_points: for p in graph_points: p.update_position(np.array(graph_points[p].estimate())) p.update_normal_and_depth(force=True) active_edges = num_edges-num_bad_edges mean_squared_error = opt.active_chi2()/active_edges return mean_squared_error, num_bad_observations/max(num_edges,1)
def optimize(frames, points, local_window, fix_points, verbose=False, rounds=50): if local_window is None: local_frames = frames else: local_frames = frames[-local_window:] # create g2o optimizer opt = g2o.SparseOptimizer() solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3()) solver = g2o.OptimizationAlgorithmLevenberg(solver) opt.set_algorithm(solver) # add normalized camera cam = g2o.CameraParameters(1.0, (0.0, 0.0), 0) cam.set_id(0) opt.add_parameter(cam) robust_kernel = g2o.RobustKernelHuber(np.sqrt(5.991)) graph_frames, graph_points = {}, {} # add frames to graph for f in (local_frames if fix_points else frames): pose = f.pose se3 = g2o.SE3Quat(pose[0:3, 0:3], pose[0:3, 3]) v_se3 = g2o.VertexSE3Expmap() v_se3.set_estimate(se3) v_se3.set_id(f.id * 2) v_se3.set_fixed(f.id <= 1 or f not in local_frames) #v_se3.set_fixed(f.id != 0) opt.add_vertex(v_se3) # confirm pose correctness est = v_se3.estimate() assert np.allclose(pose[0:3, 0:3], est.rotation().matrix()) assert np.allclose(pose[0:3, 3], est.translation()) graph_frames[f] = v_se3 # add points to frames for p in points: if not any([f in local_frames for f in p.frames]): continue pt = g2o.VertexSBAPointXYZ() pt.set_id(p.id * 2 + 1) pt.set_estimate(p.pt[0:3]) pt.set_marginalized(True) pt.set_fixed(fix_points) opt.add_vertex(pt) graph_points[p] = pt # add edges for f, idx in zip(p.frames, p.idxs): if f not in graph_frames: continue edge = g2o.EdgeProjectXYZ2UV() edge.set_parameter_id(0, 0) edge.set_vertex(0, pt) edge.set_vertex(1, graph_frames[f]) edge.set_measurement(f.kps[idx]) edge.set_information(np.eye(2)) edge.set_robust_kernel(robust_kernel) opt.add_edge(edge) if verbose: opt.set_verbose(True) opt.initialize_optimization() opt.optimize(rounds) # put frames back for f in graph_frames: est = graph_frames[f].estimate() R = est.rotation().matrix() t = est.translation() f.pose = poseRt(R, t) # put points back if not fix_points: for p in graph_points: p.pt = np.array(graph_points[p].estimate()) return opt.active_chi2()
def optimize(self, local_window=20, fix_points=False, verbose=False): # create g2o optimizer opt = g2o.SparseOptimizer() solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3()) solver = g2o.OptimizationAlgorithmLevenberg(solver) opt.set_algorithm(solver) if self.alt: principal_point = (self.frames[0].K[0][2], self.frames[0].K[1][2]) cam = g2o.CameraParameters(self.frames[0].K[0][0], principal_point, 0) cam.set_id(0) opt.add_parameter(cam) robust_kernel = g2o.RobustKernelHuber(np.sqrt(5.991)) if local_window is None: local_frames = self.frames else: local_frames = self.frames[-local_window:] graph_frames, graph_points = {}, {} # add frames to graph for f in (local_frames if fix_points else self.frames): if not self.alt: pose = np.linalg.inv(f.pose) se3 = g2o.SE3Quat(pose[0:3, 0:3], pose[0:3, 3]) sbacam = g2o.SBACam(se3) sbacam.set_cam(f.K[0][0], f.K[1][1], f.K[0][2], f.K[1][2], 0.0) v_se3 = g2o.VertexCam() v_se3.set_estimate(sbacam) else: pose = f.pose se3 = g2o.SE3Quat(pose[0:3, 0:3], pose[0:3, 3]) v_se3 = g2o.VertexSE3Expmap() v_se3.set_estimate(se3) v_se3.set_id(f.id * 2) v_se3.set_fixed(f.id <= 1 or f not in local_frames) # v_se3.set_fixed(f.id != 0) opt.add_vertex(v_se3) # confirm pose correctness est = v_se3.estimate() assert np.allclose(pose[0:3, 0:3], est.rotation().matrix()) assert np.allclose(pose[0:3, 3], est.translation()) graph_frames[f] = v_se3 # add points to frames for p in self.points: if not any([f in local_frames for f in p.frames]): continue pt = g2o.VertexSBAPointXYZ() pt.set_id(p.id * 2 + 1) pt.set_estimate(p.loc[0:3]) pt.set_marginalized(True) pt.set_fixed(fix_points) opt.add_vertex(pt) graph_points[p] = pt # add edges for f, idx in zip(p.frames, p.idxs): if f not in graph_frames: continue if not self.alt: edge = g2o.EdgeProjectP2MC() else: edge = g2o.EdgeProjectXYZ2UV() edge.set_parameter_id(0, 0) edge.set_vertex(0, pt) edge.set_vertex(1, graph_frames[f]) uv = f.raw_pts[idx] edge.set_measurement(uv) edge.set_information(np.eye(2)) edge.set_robust_kernel(robust_kernel) opt.add_edge(edge) if verbose: opt.set_verbose(True) opt.initialize_optimization() opt.optimize(50) # put frames back for f in graph_frames: est = graph_frames[f].estimate() R = est.rotation().matrix() t = est.translation() if not self.alt: f.pose = np.linalg.inv(pose_homogeneous(R, t)) else: f.pose = pose_homogeneous(R, t) # put points back (and cull) if not fix_points: culled_pt_count = 0 for p in graph_points: est = graph_points[p].estimate() p.pt = np.array(est) # remove points if the number of observations <= (n-1) old_point = len( p.frames) <= 9 and p.frames[-1].id + 17 < self.max_frame # compute reprojection error errs = [] for f, idx in zip(p.frames, p.idxs): uv = f.raw_pts[idx] proj = np.dot(np.dot(f.K, f.pose[:3]), np.array([est[0], est[1], est[2], 1.0])) proj = proj[0:2] / proj[2] errs.append(np.linalg.norm(proj - uv)) # cull if old_point or np.mean(errs) > 2: culled_pt_count += 1 self.points.remove(p) p.delete() print("Culled: %d points" % culled_pt_count) return opt.active_chi2()
def main(): optimizer = g2o.SparseOptimizer() solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3()) solver = g2o.OptimizationAlgorithmLevenberg(solver) optimizer.set_algorithm(solver) true_points = np.hstack([ np.random.random((500, 1)) * 3 - 1.5, np.random.random((500, 1)) - 0.5, np.random.random((500, 1)) + 3 ]) focal_length = (500, 500) principal_point = (320, 240) baseline = 0.075 for frame_id in range(args.n_sensors): g2o.VertexCamRig.set_cam(frame_id, *focal_length, *principal_point) sensor_pose = g2o.Isometry3d(np.identity(3), [frame_id * baseline, 0, 0]) g2o.VertexCamRig.set_calibration(frame_id, sensor_pose) true_poses = [] num_pose = 5 for i in range(num_pose): # pose here transform points from world coordinates to camera coordinates pose = g2o.Isometry3d(np.identity(3), [i * 0.04 - 1, 0, 0]) true_poses.append(pose) v_se3 = g2o.VertexCamRig() v_se3.set_id(i) v_se3.set_estimate(pose) if i < 2: v_se3.set_fixed(True) optimizer.add_vertex(v_se3) point_id = num_pose inliers = dict() sse = defaultdict(float) for i, point in enumerate(true_points): visible = [] for j in range(num_pose): sens_vis = 0 projections = [] for frame_id in range(args.n_sensors): z = optimizer.vertex(j).map_point(frame_id, point) if 0 <= z[0] < 640 and 0 <= z[1] < 480: projections.append((frame_id, z)) if len(projections) > 1: visible.append((j, projections)) if len(visible) < 2: continue vp = g2o.VertexSBAPointXYZ() vp.set_id(point_id) vp.set_marginalized(True) vp.set_estimate(point + np.random.randn(3)) optimizer.add_vertex(vp) inlier = True if np.random.random() < args.outlier_ratio: inlier = False if inlier: inliers[point_id] = i error = vp.estimate() - true_points[i] sse[0] += np.sum(error**2) for j, projections in visible: for frame_id, z in projections: if not inliers: z = np.array([ np.random.uniform(64, 640), np.random.uniform(0, 480) ]) z += np.random.randn(2) * args.pixel_noise * [1, 1] edge = g2o.Edge_XYZ_VRIG() edge.set_vertex(0, vp) edge.set_vertex(1, optimizer.vertex(j)) edge.set_measurement(np.hstack((z, [frame_id]))) edge.set_information(np.identity(3)) if args.robust_kernel: edge.set_robust_kernel(g2o.RobustKernelHuber()) edge.set_parameter_id(0, 0) optimizer.add_edge(edge) point_id += 1 print('num points', len(inliers)) print('Performing full BA:') optimizer.initialize_optimization() optimizer.set_verbose(True) optimizer.optimize(10) for i in inliers: vp = optimizer.vertex(i) error = vp.estimate() - true_points[inliers[i]] sse[1] += np.sum(error**2) print('\nRMSE (inliers only):') print('before optimization:', np.sqrt(sse[0] / len(inliers))) print('after optimization:', np.sqrt(sse[1] / len(inliers))) sse = defaultdict(float) for i, gt_pose in enumerate(true_poses): vp = optimizer.vertex(i) error = vp.estimate().translation() - pose.translation() sse[1] += np.sum(error**2) print('\nRMSE (inliers only):') print('pose error before optimization:', np.sqrt(sse[0] / len(inliers))) print('pose error after optimization:', np.sqrt(sse[1] / len(inliers)))
def optimization(frames, points, local_window, fixed_points=False, verbose=False, rounds=40, use_robust_kernel=False): if local_window is None: local_frames = frames else: local_frames = frames[-local_window:] # create g2o optimizer opt = g2o.SparseOptimizer() solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3()) solver = g2o.OptimizationAlgorithmLevenberg(solver) opt.set_algorithm(solver) thHuberMono = math.sqrt(5.991) # chi-square 2 DOFS graph_frames, graph_points = {}, {} # add frame vertices to graph for f in ( local_frames if fixed_points else frames ): # if points are fixed then consider just the local frames, otherwise we need all frames or at least two frames for each point #print('adding vertex frame ', f.id, ' to graph') pose = f.pose se3 = g2o.SE3Quat(pose[0:3, 0:3], pose[0:3, 3]) v_se3 = g2o.VertexSE3Expmap() v_se3.set_estimate(se3) v_se3.set_id(f.id * 2) # even ids v_se3.set_fixed(f.id < 1 or f not in local_frames) opt.add_vertex(v_se3) # confirm pose correctness #est = v_se3.estimate() #assert np.allclose(pose[0:3, 0:3], est.rotation().matrix()) #assert np.allclose(pose[0:3, 3], est.translation()) graph_frames[f] = v_se3 # add point vertices to graph for p in points: if p.is_bad and not fixed_points: continue if not any([f in local_frames for f in p.frames]): continue #print('adding vertex point ', p.id,' to graph') v_p = g2o.VertexSBAPointXYZ() v_p.set_id(p.id * 2 + 1) # odd ids v_p.set_estimate(p.pt[0:3]) v_p.set_marginalized(True) v_p.set_fixed(fixed_points) opt.add_vertex(v_p) graph_points[p] = v_p # add edges for f, idx in zip(p.frames, p.idxs): if f not in graph_frames: continue #print('adding edge between point ', p.id,' and frame ', f.id) edge = g2o.EdgeSE3ProjectXYZ() edge.set_vertex(0, v_p) edge.set_vertex(1, graph_frames[f]) edge.set_measurement(f.kpsu[idx]) invSigma2 = Frame.detector.inv_level_sigmas2[f.octaves[idx]] edge.set_information(np.eye(2) * invSigma2) if use_robust_kernel: edge.set_robust_kernel(g2o.RobustKernelHuber(thHuberMono)) edge.fx = f.fx edge.fy = f.fy edge.cx = f.cx edge.cy = f.cy opt.add_edge(edge) if verbose: opt.set_verbose(True) opt.initialize_optimization() opt.optimize(rounds) # put frames back for f in graph_frames: est = graph_frames[f].estimate() R = est.rotation().matrix() t = est.translation() f.pose = poseRt(R, t) # put points back if not fixed_points: for p in graph_points: p.pt = np.array(graph_points[p].estimate()) return opt.active_chi2()
def localOptimization(frames, points, frames_ref=[], fixed_points=False, verbose=False, rounds=10): # create g2o optimizer opt = g2o.SparseOptimizer() solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3()) solver = g2o.OptimizationAlgorithmLevenberg(solver) opt.set_algorithm(solver) #robust_kernel = g2o.RobustKernelHuber(np.sqrt(5.991)) # chi-square 2 DOFs thHuberMono = math.sqrt(5.991) # chi-square 2 DOFS graph_frames, graph_points = {}, {} all_frames = frames + frames_ref # add frame vertices to graph for f in all_frames: #print('adding vertex frame ', f.id, ' to graph') pose = f.pose se3 = g2o.SE3Quat(pose[0:3, 0:3], pose[0:3, 3]) v_se3 = g2o.VertexSE3Expmap() v_se3.set_estimate(se3) v_se3.set_id(f.id * 2) # even ids v_se3.set_fixed(f.id < 1 or f in frames_ref) opt.add_vertex(v_se3) graph_frames[f] = v_se3 # confirm pose correctness #est = v_se3.estimate() #assert np.allclose(pose[0:3, 0:3], est.rotation().matrix()) #assert np.allclose(pose[0:3, 3], est.translation()) graph_edges = {} num_point_edges = 0 # add point vertices to graph for p in points: assert (p is not None) if p.is_bad and not fixed_points: # do not consider bad points unless they are fixed continue if not any([f in frames for f in p.frames]): # this is redundant now continue #print('adding vertex point ', p.id,' to graph') v_p = g2o.VertexSBAPointXYZ() v_p.set_id(p.id * 2 + 1) # odd ids v_p.set_estimate(p.pt[0:3]) v_p.set_marginalized(True) v_p.set_fixed(fixed_points) opt.add_vertex(v_p) graph_points[p] = v_p # add edges for f, p_idx in zip(p.frames, p.idxs): assert (f.points[p_idx] == p) if f not in graph_frames: continue #print('adding edge between point ', p.id,' and frame ', f.id) edge = g2o.EdgeSE3ProjectXYZ() edge.set_vertex(0, v_p) edge.set_vertex(1, graph_frames[f]) edge.set_measurement(f.kpsu[p_idx]) invSigma2 = Frame.detector.inv_level_sigmas2[f.octaves[p_idx]] edge.set_information(np.eye(2) * invSigma2) edge.set_robust_kernel(g2o.RobustKernelHuber(thHuberMono)) edge.fx = f.fx edge.fy = f.fy edge.cx = f.cx edge.cy = f.cy opt.add_edge(edge) graph_edges[edge] = (p, f, p_idx) # f.points[p_idx] == p num_point_edges += 1 if verbose: opt.set_verbose(True) # initial optimization opt.initialize_optimization() opt.optimize(5) chi2Mono = 5.991 # chi-square 2 DOFs # check inliers observation for edge, edge_data in graph_edges.items(): p = edge_data[0] if p.is_bad is True: continue if edge.chi2() > chi2Mono or not edge.is_depth_positive(): edge.set_level(1) edge.set_robust_kernel(None) # optimize again without outliers opt.initialize_optimization() opt.optimize(rounds) # clean map observations num_bad_observations = 0 outliers_data = [] for edge, edge_data in graph_edges.items(): p, f, p_idx = edge_data if p.is_bad is True: continue assert (f.points[p_idx] == p) if edge.chi2() > chi2Mono or not edge.is_depth_positive(): num_bad_observations += 1 outliers_data.append((p, f, p_idx)) for d in outliers_data: (p, f, p_idx) = d assert (f.points[p_idx] == p) p.remove_observation(f, p_idx) f.remove_point(p) # put frames back for f in graph_frames: est = graph_frames[f].estimate() R = est.rotation().matrix() t = est.translation() f.pose = poseRt(R, t) # put points back if not fixed_points: for p in graph_points: p.pt = np.array(graph_points[p].estimate()) return opt.active_chi2(), num_bad_observations / num_point_edges
def poseOptimization(frame, verbose=False, rounds=10): is_ok = True # create g2o optimizer opt = g2o.SparseOptimizer() solver = g2o.BlockSolverSE3(g2o.LinearSolverCSparseSE3()) solver = g2o.OptimizationAlgorithmLevenberg(solver) opt.set_algorithm(solver) #robust_kernel = g2o.RobustKernelHuber(np.sqrt(5.991)) # chi-square 2 DOFs thHuberMono = math.sqrt(5.991) # chi-square 2 DOFS point_edge_pairs = {} num_point_edges = 0 se3 = g2o.SE3Quat(frame.pose[0:3, 0:3], frame.pose[0:3, 3]) v_se3 = g2o.VertexSE3Expmap() v_se3.set_estimate(se3) v_se3.set_id(0) v_se3.set_fixed(False) opt.add_vertex(v_se3) # add point vertices to graph for idx, p in enumerate(frame.points): if p is None: # do not use p.is_bad here since a single point observation is ok for pose optimization continue frame.outliers[idx] = False # add edge #print('adding edge between point ', p.id,' and frame ', frame.id) edge = g2o.EdgeSE3ProjectXYZOnlyPose() edge.set_vertex(0, opt.vertex(0)) edge.set_measurement(frame.kpsu[idx]) invSigma2 = Frame.detector.inv_level_sigmas2[frame.octaves[idx]] edge.set_information(np.eye(2) * invSigma2) edge.set_robust_kernel(g2o.RobustKernelHuber(thHuberMono)) edge.fx = frame.fx edge.fy = frame.fy edge.cx = frame.cx edge.cy = frame.cy edge.Xw = p.pt[0:3] opt.add_edge(edge) point_edge_pairs[p] = (edge, idx) # one edge per point num_point_edges += 1 if num_point_edges < 3: Printer.red('poseOptimization: not enough correspondences!') is_ok = False return 0, is_ok, 0 if verbose: opt.set_verbose(True) # We perform 4 optimizations, after each optimization we classify observation as inlier/outlier # At the next optimization, outliers are not included, but at the end they can be classified as inliers again. chi2Mono = 5.991 # chi-square 2 DOFs num_bad_points = 0 for it in range(4): opt.initialize_optimization() opt.optimize(rounds) num_bad_points = 0 for p, edge_pair in point_edge_pairs.items(): if frame.outliers[edge_pair[1]] is True: edge_pair[0].compute_error() chi2 = edge_pair[0].chi2() if chi2 > chi2Mono: frame.outliers[edge_pair[1]] = True edge_pair[0].set_level(1) num_bad_points += 1 else: frame.outliers[edge_pair[1]] = False edge_pair[0].set_level(0) if it == 2: edge_pair[0].set_robust_kernel(None) if len(opt.edges()) < 10: Printer.red('poseOptimization: stopped - not enough edges!') is_ok = False break print('pose optimization: initial ', num_point_edges, ' points, found ', num_bad_points, ' bad points') if num_point_edges == num_bad_points: Printer.red( 'poseOptimization: all the initial correspondences are bad!') is_ok = False # update pose estimation if is_ok is True: est = v_se3.estimate() R = est.rotation().matrix() t = est.translation() frame.pose = poseRt(R, t) num_valid_points = num_point_edges - num_bad_points return opt.active_chi2(), is_ok, num_valid_points