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| | calibration_estimation.error_visualization.a |
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| | calibration_estimation.error_visualization.action |
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| | calibration_estimation.error_visualization.all_sensor_types = list(set([x['sensor_type'] for x in all_sensors_dict.values()])) |
| |
| | calibration_estimation.error_visualization.all_sensors_dict = est_helpers.build_sensor_defs(config[sensors_name]) |
| |
| | calibration_estimation.error_visualization.b |
| |
| | calibration_estimation.error_visualization.bag_filename = rospy.myargv()[1] |
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| list | calibration_estimation.error_visualization.cam_covs = [matrix(array(s.compute_cov(fk)) * kron(eye(s.get_residual_length()/2),ones([2,2]))) for s,fk in zip(cam_sensors, fk_points)] |
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| list | calibration_estimation.error_visualization.cam_Js = [s.compute_expected_J(fk) for s,fk in zip(cam_sensors, fk_points)] |
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| list | calibration_estimation.error_visualization.cam_sensors = [[s for s in ms.sensors if s.sensor_id == cur_loop['cam']][0] for ms in multisensors_pruned] |
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| list | calibration_estimation.error_visualization.cb_points = [SingleTransform(pose).transform * system_def.checkerboards[ms.checkerboard].generate_points() for pose, ms in zip(cb_poses_pruned,multisensors_pruned)] |
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| | calibration_estimation.error_visualization.cb_poses = yaml.load(open(output_dir + "/" + cur_step["output_filename"] + "_poses.yaml")) |
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| | calibration_estimation.error_visualization.cb_poses_pruned |
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| list | calibration_estimation.error_visualization.chain_sensors = [[s for s in ms.sensors if s.sensor_id == cur_loop['3d']][0] for ms in multisensors_pruned] |
| |
| | calibration_estimation.error_visualization.config = rospy.get_param(config_param_name) |
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| string | calibration_estimation.error_visualization.config_param_name = "calibration_config" |
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| | calibration_estimation.error_visualization.cur_scatter = plt.scatter(array(r)[:,0], array(r)[:,1], **cur_loop['plot_ops']) |
| |
| | calibration_estimation.error_visualization.cur_step = step_list[-1] |
| |
| | calibration_estimation.error_visualization.e = error_calc.calculate_error(opt_all_vec) |
| |
| | calibration_estimation.error_visualization.error_calc = opt_runner.ErrorCalc(system_def, free_dict, multisensors_pruned, False) |
| |
| | calibration_estimation.error_visualization.error_cat = numpy.concatenate(error_list) |
| |
| | calibration_estimation.error_visualization.errors_dict = opt_runner.compute_errors_breakdown(error_calc, multisensors_pruned, numpy.array(cb_poses_pruned)) |
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| list | calibration_estimation.error_visualization.fk_covs = [matrix(array(s.compute_cov(None)) * kron(eye(s.get_residual_length()/3),ones([3,3]))) for s in chain_sensors] |
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| list | calibration_estimation.error_visualization.fk_points = [s.get_measurement() for s in chain_sensors] |
| |
| | calibration_estimation.error_visualization.frame_id |
| |
| | calibration_estimation.error_visualization.free_dict = yaml.load(cur_step["free_params"]) |
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| list | calibration_estimation.error_visualization.full_covs = [matrix(cam_J)*fk_cov*matrix(cam_J).T + cam_cov for cam_J, cam_cov, fk_cov in zip(cam_Js, cam_covs, fk_covs)] |
| |
| | calibration_estimation.error_visualization.g |
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| int | calibration_estimation.error_visualization.i = 0 |
| |
| | calibration_estimation.error_visualization.id |
| |
| | calibration_estimation.error_visualization.label_list = list() |
| |
| | calibration_estimation.error_visualization.loop_list = yaml.load(open(loop_list_filename)) |
| |
| | calibration_estimation.error_visualization.loop_list_filename = rospy.myargv()[3] |
| |
| | calibration_estimation.error_visualization.m = Marker() |
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| int | calibration_estimation.error_visualization.marker_count = 0 |
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| | calibration_estimation.error_visualization.marker_pub = rospy.Publisher("calibration_error", Marker) |
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| list | calibration_estimation.error_visualization.meas_points = [s.get_measurement() for s in cam_sensors] |
| |
| | calibration_estimation.error_visualization.multisensors = get_multisensors(bag_filename, sensor_defs, sample_skip_list) |
| |
| | calibration_estimation.error_visualization.multisensors_pruned |
| |
| | calibration_estimation.error_visualization.ns |
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| | calibration_estimation.error_visualization.opt_all_vec = opt_runner.build_opt_vector(system_def, free_dict, numpy.array(cb_poses_pruned)) |
| |
| | calibration_estimation.error_visualization.output_dir = rospy.myargv()[2] |
| |
| | calibration_estimation.error_visualization.points |
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| list | calibration_estimation.error_visualization.points_list_fk = [ geometry_msgs.msg.Point(cur_pt[0, 0], cur_pt[0, 1], cur_pt[0, 2]) for cur_pt in list(numpy.concatenate(fk_points,1).T)] |
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| list | calibration_estimation.error_visualization.points_list_guess = [ geometry_msgs.msg.Point(cur_pt[0, 0], cur_pt[0, 1], cur_pt[0, 2]) for cur_pt in list(numpy.concatenate(cb_points,1).T)] |
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| list | calibration_estimation.error_visualization.proj_points = [s.compute_expected(pts) for (s,pts) in zip(cam_sensors,fk_points)] |
| |
| | calibration_estimation.error_visualization.prop |
| |
| | calibration_estimation.error_visualization.r = numpy.concatenate((r1,r2),axis=0) |
| |
| | calibration_estimation.error_visualization.r1 = numpy.concatenate(meas_points) |
| |
| | calibration_estimation.error_visualization.r2 = numpy.concatenate(proj_points) |
| |
| | calibration_estimation.error_visualization.rms_error = numpy.sqrt( numpy.mean(error**2) ) |
| |
| | calibration_estimation.error_visualization.robot_description = get_robot_description(bag_filename) |
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| list | calibration_estimation.error_visualization.sample_ind = [k for k,ms in zip(range(len(multisensors)), multisensors) if len(ms.sensors) == 2] |
| |
| | calibration_estimation.error_visualization.sample_skip_list = rospy.get_param('calibration_skip_list', []) |
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| list | calibration_estimation.error_visualization.scatter_list = [] |
| |
| | calibration_estimation.error_visualization.sensor_defs = est_helpers.load_requested_sensors(all_sensors_dict, [cur_loop['cam'], cur_loop['3d']]) |
| |
| string | calibration_estimation.error_visualization.sensors_name = "sensors" |
| |
| | calibration_estimation.error_visualization.step_list = est_helpers.load_calibration_steps(config["cal_steps"]) |
| |
| | calibration_estimation.error_visualization.system_def = UrdfParams(robot_description, system_def_dict) |
| |
| | calibration_estimation.error_visualization.system_def_dict = yaml.load(open(output_dir + "/" + cur_step["output_filename"] + ".yaml")) |
| |
| | calibration_estimation.error_visualization.type |
| |
| | calibration_estimation.error_visualization.x |
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| | calibration_estimation.error_visualization.y |
| |
| | calibration_estimation.error_visualization.z |
| |