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read_bag.py
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read_bag.py
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import rosbag
import numpy as np
import utm as utm_tf
from scipy.spatial.transform import Rotation
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
import open3d
import copy
import gc
import util
import constant
imu_topic = '/imu/imu'
left_lidar_topic = '/ns1/velodyne_points'
right_lidar_topic = '/ns2/velodyne_points'
gps_topic = '/vehicle/gps/fix'
imu_translation = np.array([-0.15, -0.66, 0])
left_lidar_translation = np.array([-0.1, 0.0, 0.062 + 0.037])
right_lidar_translation = np.array([-0.1, -1.32, 0.062 + 0.037])
self_box = np.array([[-4.839+2.2, 2.2], [-1.834/2, 1.834/2],
[-1.45-0.16, 0]]).T+imu_translation
plt.ion()
fig = plt.figure(figsize=(8, 8))
ax = Axes3D(fig)
ax.dist = 30
init_imu_point = np.append(imu_translation, 1)
utm_list = []
with rosbag.Bag('test_3.bag') as bag:
quat_list = []
acc_list = []
gyr_list = []
imu_time_list = []
left_lidar_points = None
prev_point_cloud = None
prev_point_cloud_fpfh = None
prev_acc = None
prev_vel = None
plot_position_list = []
count = 0
for topic, msg, t in bag.read_messages(
topics=[imu_topic, left_lidar_topic, right_lidar_topic, gps_topic]):
# print(t.to_sec())
if topic == imu_topic:
quat_list.append([msg.orientation.x,
msg.orientation.y,
msg.orientation.z,
msg.orientation.w])
acc_list.append(
[msg.linear_acceleration.x,
msg.linear_acceleration.y,
msg.linear_acceleration.z])
gyr_list.append(
[msg.angular_velocity.x,
msg.angular_velocity.y,
msg.angular_velocity.z])
imu_time_list.append(msg.header.stamp.to_sec())
elif topic == left_lidar_topic:
left_lidar_points = util.get_msg_lidar_points(
msg)
left_lidar_points = util.transform_lidar_points(
left_lidar_points, left_lidar_translation)
elif topic == right_lidar_topic and left_lidar_points is not None:
right_lidar_points = util.get_msg_lidar_points(
msg)
right_lidar_points = util.transform_lidar_points(
right_lidar_points, right_lidar_translation)
lidar_points = np.vstack((left_lidar_points, right_lidar_points))
point_cloud = util.get_point_cloud(lidar_points)
point_cloud = point_cloud.voxel_down_sample(
voxel_size=constant.voxel_size)
_, inlier_indexes = point_cloud.remove_statistical_outlier(nb_neighbors=20,
std_ratio=1.0)
plot_noisy_points = np.array(point_cloud.select_down_sample(
inlier_indexes, invert=True).points)
point_cloud = point_cloud.select_down_sample(inlier_indexes)
lidar_points = lidar_points[inlier_indexes]
ground_mask = util.get_ground_mask(
lidar_points, self_box)
plot_ground_points = lidar_points[ground_mask]
lidar_points = lidar_points[~ground_mask]
point_cloud.points = open3d.utility.Vector3dVector(
lidar_points)
labels = np.array(
point_cloud.cluster_dbscan(eps=1, min_points=10))
plot_noisy_points = np.vstack(
(plot_noisy_points, lidar_points[labels == -1]))
lidar_points = lidar_points[labels != -1]
labels = labels[labels != -1]
self_mask = util.get_self_mask(
lidar_points, labels, self_box)
plot_self_points = lidar_points[self_mask]
lidar_points = lidar_points[~self_mask]
labels = labels[~self_mask]
point_cloud.points = open3d.utility.Vector3dVector(
lidar_points)
point_cloud_fpfh = util.get_point_cloud_fpfh(point_cloud)
if prev_point_cloud is not None:
registration_result = util.get_registration_result(
prev_point_cloud, point_cloud, prev_point_cloud_fpfh, point_cloud_fpfh)
transformation = np.array(registration_result.transformation)
if prev_vel is None:
prev_vel = np.linalg.inv(transformation)[:3, -1]
transformation = np.identity(4)
ypr = util.quat_to_ypr(quat_list[-1])
ypr[0] -= np.deg2rad(90+14.4-43)
transformation[:3, :3] = Rotation.from_euler(
'zyx', ypr).as_dcm()
transformation_list = [transformation]
point_cloud_mapping = open3d.geometry.PointCloud()
else:
# quat_list = np.array(quat_list)
# acc_list = np.array(acc_list)
# gyr_list = np.array(gyr_list)
# imu_time_list = np.array(imu_time_list)
# ypr_list = np.apply_along_axis(
# util.quat_to_ypr, -1, quat_list)
# ypr_list[:, 0] -= np.deg2rad(90+14.4-43)
# dcm_list = np.apply_along_axis(
# util.ypr_to_dcm, -1, ypr_list)
# lidar_acc_list = []
# for index, (dcm, acc) in enumerate(zip(dcm_list, acc_list)):
# acc = np.linalg.inv(dcm).dot(acc)
# acc[2] += 9.81
# lidar_acc_list.append(acc)
prev_point_cloud.transform(transformation)
moving_labels = util.get_moving_labels(np.array(prev_point_cloud.points), np.array(point_cloud.points),
np.array(registration_result.correspondence_set), labels, self_box)
plot_moving_points = lidar_points[moving_labels != -1]
lidar_points = lidar_points[moving_labels == -1]
moving_labels = moving_labels[moving_labels != -1]
# point_cloud.points = open3d.utility.Vector3dVector(
# lidar_points)
transformation = transformation.dot(
transformation_list[-1])
transformation_list.append(transformation)
# static_point_cloud = util.get_point_cloud(lidar_points)
# static_point_cloud.transform(
# np.linalg.inv(transformation))
# point_cloud_mapping += static_point_cloud
ax.clear()
plot_start_self_box_points, plot_end_self_box_points = util.get_start_end_box_points(
self_box)
plot_start_self_box_points = util.transform_points(
transformation, plot_start_self_box_points)
plot_end_self_box_points = util.transform_points(
transformation, plot_end_self_box_points)
util.plot_box_lines(ax, plot_start_self_box_points,
plot_end_self_box_points, 'green')
plot_ground_points = util.transform_points(
transformation, plot_ground_points)
ax.scatter(plot_ground_points[:, 0], plot_ground_points[:, 1],
plot_ground_points[:, 2], c='orange', s=1)
plot_noisy_points = util.transform_points(
transformation, plot_noisy_points)
ax.scatter(plot_noisy_points[:, 0], plot_noisy_points[:, 1],
plot_noisy_points[:, 2], c='gray', s=1)
plot_self_points = util.transform_points(
transformation, plot_self_points)
ax.scatter(plot_self_points[:, 0], plot_self_points[:, 1],
plot_self_points[:, 2], c='green', s=1)
# plot_moving_labels = moving_labels[moving_labels != -2]
# if len(plot_moving_labels) != 0:
# plot_start_moving_box_points, plot_end_moving_box_points = util.get_moving_box_points(
# plot_moving_points[moving_labels != -2], plot_moving_labels)
# plot_start_moving_box_points = util.transform_points(
# transformation, plot_start_moving_box_points)
# plot_end_moving_box_points = util.transform_points(
# transformation, plot_end_moving_box_points)
# util.plot_box_lines(ax, plot_start_moving_box_points,
# plot_end_moving_box_points, 'darkgreen')
plot_moving_points = util.transform_points(
transformation, plot_moving_points)
ax.scatter(plot_moving_points[:, 0], plot_moving_points[:, 1],
plot_moving_points[:, 2], c='darkgreen', s=1)
plot_lidar_points = util.transform_points(
transformation, lidar_points)
ax.scatter(plot_lidar_points[:, 0], plot_lidar_points[:, 1],
plot_lidar_points[:, 2], c='red', s=1)
plot_imu_point = np.linalg.inv(
transformation).dot(init_imu_point)[:3]
plot_position_list.append(plot_imu_point)
temp_plot_position_list = np.array(plot_position_list)
ax.scatter(temp_plot_position_list[:, 0], temp_plot_position_list[:, 1],
temp_plot_position_list[:, 2])
# mean_acc_list = np.mean(lidar_acc_list, axis=0)
# ax.quiver(plot_imu_point[0], plot_imu_point[1],
# plot_imu_point[2], mean_acc_list[0], mean_acc_list[1], 0)
if len(utm_list) != 0:
plot_utm_list = np.array(utm_list)
plot_utm_list = plot_utm_list-plot_utm_list[0]
ax.scatter(plot_utm_list[:, 0],
plot_utm_list[:, 1], marker='x', s=100, c='blue')
ax.set_axis_off()
ax.set_xlim3d(-10+plot_imu_point[0], 10+plot_imu_point[0])
ax.set_ylim3d(-10+plot_imu_point[1], 10+plot_imu_point[1])
ax.set_zlim3d(-10+plot_imu_point[2], 10+plot_imu_point[2])
# ax.set_xlim3d(-20, 20)
# ax.set_ylim3d(-20, 20)
# ax.set_zlim3d(-20, 20)
ax.view_init(30, 165-Rotation.from_dcm(
transformation_list[-1][:3, :3]).as_euler('zyx', degrees=True)[0])
# ax.view_init(90, -90)
plt.pause(0.1)
# if count == 150:
# point_cloud_mapping = point_cloud_mapping.voxel_down_sample(
# voxel_size=constant.voxel_size)
# print(len(point_cloud_mapping.points))
# open3d.io.write_point_cloud(
# "mapping.pcd", point_cloud_mapping)
# open3d.visualization.draw_geometries(
# [point_cloud_mapping])
# break
count += 1
print(count)
quat_list = [quat_list[-1]]
acc_list = [acc_list[-1]]
gyr_list = [gyr_list[-1]]
imu_time_list = [imu_time_list[-1]]
prev_point_cloud = point_cloud
prev_point_cloud_fpfh = point_cloud_fpfh
elif topic == gps_topic:
utm_list.append(utm_tf.from_latlon(
msg.latitude, msg.longitude)[:2])