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error_visualization.py File Reference

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Namespaces

namespace  calibration_estimation::error_visualization

Functions

def calibration_estimation::error_visualization.usage

Variables

tuple calibration_estimation::error_visualization.all_sensor_types = list(set([x['sensor_type'] for x in all_sensors_dict.values()]))
tuple calibration_estimation::error_visualization.all_sensors_dict = est_helpers.build_sensor_defs(config[sensors_name])
tuple calibration_estimation::error_visualization.bag_filename = rospy.myargv()
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)]
list calibration_estimation::error_visualization.cam_Js = [s.compute_expected_J(fk) for s,fk in zip(cam_sensors, fk_points)]
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]
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)]
tuple calibration_estimation::error_visualization.cb_poses = yaml.load(open(output_dir + "/" + cur_step["output_filename"] + "_poses.yaml"))
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]
tuple calibration_estimation::error_visualization.config = rospy.get_param(config_param_name)
string calibration_estimation::error_visualization.config_param_name = "calibration_config"
tuple calibration_estimation::error_visualization.cur_scatter = plt.scatter(array(r)[:,0], array(r)[:,1], **cur_loop['plot_ops'])
list calibration_estimation::error_visualization.cur_step = step_list[-1]
tuple calibration_estimation::error_visualization.e = error_calc.calculate_error(opt_all_vec)
tuple calibration_estimation::error_visualization.error_calc = opt_runner.ErrorCalc(system_def, free_dict, multisensors_pruned, False)
tuple calibration_estimation::error_visualization.error_cat = numpy.concatenate(error_list)
tuple calibration_estimation::error_visualization.errors_dict = opt_runner.compute_errors_breakdown(error_calc, multisensors_pruned, numpy.array(cb_poses_pruned))
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]
list calibration_estimation::error_visualization.fk_points = [s.get_measurement() for s in chain_sensors]
tuple calibration_estimation::error_visualization.free_dict = yaml.load(cur_step["free_params"])
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)]
int calibration_estimation::error_visualization.i = 0
tuple calibration_estimation::error_visualization.label_list = list()
tuple calibration_estimation::error_visualization.loop_list = yaml.load(open(loop_list_filename))
tuple calibration_estimation::error_visualization.loop_list_filename = rospy.myargv()
tuple calibration_estimation::error_visualization.m = Marker()
int calibration_estimation::error_visualization.marker_count = 0
tuple calibration_estimation::error_visualization.marker_pub = rospy.Publisher("calibration_error", Marker)
list calibration_estimation::error_visualization.meas_points = [s.get_measurement() for s in cam_sensors]
tuple calibration_estimation::error_visualization.multisensors = get_multisensors(bag_filename, sensor_defs, sample_skip_list)
tuple calibration_estimation::error_visualization.opt_all_vec = opt_runner.build_opt_vector(system_def, free_dict, numpy.array(cb_poses_pruned))
tuple calibration_estimation::error_visualization.output_dir = rospy.myargv()
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)]
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)]
list calibration_estimation::error_visualization.proj_points = [s.compute_expected(pts) for (s,pts) in zip(cam_sensors,fk_points)]
tuple calibration_estimation::error_visualization.r = numpy.concatenate(proj_points)
tuple calibration_estimation::error_visualization.rms_error = numpy.sqrt( numpy.mean(error**2) )
tuple calibration_estimation::error_visualization.robot_description = get_robot_description(bag_filename)
list calibration_estimation::error_visualization.sample_ind = [k for k,ms in zip(range(len(multisensors)), multisensors) if len(ms.sensors) == 2]
tuple calibration_estimation::error_visualization.sample_skip_list = rospy.get_param('calibration_skip_list', [])
list calibration_estimation::error_visualization.scatter_list = []
tuple 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"
tuple calibration_estimation::error_visualization.step_list = est_helpers.load_calibration_steps(config["cal_steps"])
tuple calibration_estimation::error_visualization.system_def = UrdfParams(robot_description, system_def_dict)
tuple calibration_estimation::error_visualization.system_def_dict = yaml.load(open(output_dir + "/" + cur_step["output_filename"] + ".yaml"))
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calibration_estimation
Author(s): Vijay Pradeep, Michael Ferguson
autogenerated on Thu Aug 15 2013 10:15:40