Python中使用kitti数据集实现自动驾驶(绘制出所有物体的行驶轨迹)

这篇文章主要介绍了Python中使用kitti数据集实现自动驾驶——绘制出所有物体的行驶轨迹,本次内容主要是画出kitti车的行驶的轨迹,需要的朋友可以参考下

1、利用IMU、GPS计算汽车移动距离和旋转角度

计算移动距离
通过GPS计算

#定义计算GPS距离方法 def computer_great_circle_distance(lat1,lon1,lat2,lon2): delta_sigma = float(np.sin(lat1*np.pi/180)*np.sin(lat2*np.pi/180)+\ np.cos(lat1*np.pi/180)*np.cos(lat2*np.pi/180)*np.cos(lon1*np.pi/180-lon2*np.pi/180)) return 6371000.0*np.arccos(np.clip(delta_sigma,-1,1)) #使用GPS计算距离 gps_distance += [computer_great_circle_distance(imu_data.lat,imu_data.lon,prev_imu_data.lat,prev_imu_data.lon)]

通过IMU计算

IMU_COLUMN_NAMES = ['lat','lon','alt','roll','pitch','yaw','vn','ve','vf','vl','vu','ax','ay','az','af', 'al','au','wx','wy','wz','wf','wl','wu','posacc','velacc','navstat','numsats','posmode', 'velmode','orimode'] #获取IMU数据 imu_data = read_imu('/home/wsj/data/kitty/RawData/2011_09_26/2011_09_26_drive_0005_sync/oxts/data/%010d.txt'%frame) #使用IMU计算距离 imu_distance += [0.1*np.linalg.norm(imu_data[['vf','vl']])]

比较两种方式计算出的距离（GPS/IMU）

import matplotlib.pyplot as plt plt.figure(figsize=(20,10)) plt.plot(gps_distance, label='gps_distance') plt.plot(imu_distance, label='imu_distance') plt.legend() plt.show()

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显然，IMU计算的距离较为平滑。

计算旋转角度旋转角度的计算较为简单，我们只需要根据IMU获取到的yaw值就可以计算（前后两帧图像的yaw值相减）

2、画出kitti车的行驶轨迹

prev_imu_data = None locations = [] for frame in range(150): imu_data = read_imu('/home/wsj/data/kitty/RawData/2011_09_26/2011_09_26_drive_0005_sync/oxts/data/%010d.txt'%frame) if prev_imu_data is not None: displacement = 0.1*np.linalg.norm(imu_data[['vf','vl']]) yaw_change = float(imu_data.yaw-prev_imu_data.yaw) for i in range(len(locations)): x0, y0 = locations[i] x1 = x0 * np.cos(yaw_change) + y0 * np.sin(yaw_change) - displacement y1 = -x0 * np.sin(yaw_change) + y0 * np.cos(yaw_change) locations[i] = np.array([x1,y1]) locations += [np.array([0,0])] prev_imu_data =imu_data plt.figure(figsize=(20,10)) plt.plot(np.array(locations)[:, 0],np.array(locations)[:, 1])

3、画出所有车辆的轨迹

class Object(): def __init__(self, center): self.locations = deque(maxlen=20) self.locations.appendleft(center) def update(self, center, displacement, yaw): for i in range(len(self.locations)): x0, y0 = self.locations[i] x1 = x0 * np.cos(yaw_change) + y0 * np.sin(yaw_change) - displacement y1 = -x0 * np.sin(yaw_change) + y0 * np.cos(yaw_change) self.locations[i] = np.array([x1,y1]) if center is not None: self.locations.appendleft(center) def reset(self): self.locations = deque(maxlen=20) #创建发布者 loc_pub = rospy.Publisher('kitti_loc', MarkerArray, queue_size=10) #获取距离和旋转角度 imu_data =  read_imu('/home/wsj/data/kitty/RawData/2011_09_26/2011_09_26_drive_0005_sync/oxts/data/%010d.txt'%frame) if prev_imu_data is None: for track_id in centers: tracker[track_id] = Object(centers[track_id]) else: displacement = 0.1*np.linalg.norm(imu_data[['vf','vl']]) yaw_change = float(imu_data.yaw - prev_imu_data.yaw) for track_id in centers: # for one frame id if track_id in tracker: tracker[track_id].update(centers[track_id], displacement, yaw_change) else: tracker[track_id] = Object(centers[track_id]) for track_id in tracker:# for whole ids tracked by prev frame,but current frame did not if track_id not in centers: # dont know its center pos tracker[track_id].update(None, displacement, yaw_change) prev_imu_data = imu_data def publish_loc(loc_pub, tracker, centers): marker_array = MarkerArray() for track_id in centers: marker = Marker() marker.header.frame_id = FRAME_ID marker.header.stamp = rospy.Time.now() marker.action = marker.ADD marker.lifetime = rospy.Duration(LIFETIME) marker.type = Marker.LINE_STRIP marker.id = track_id marker.color.r = 1.0 marker.color.g = 1.0 marker.color.b = 0.0 marker.color.a = 1.0 marker.scale.x = 0.2 marker.points = [] for p in tracker[track_id].locations: marker.points.append(Point(p[0], p[1], 0)) marker_array.markers.append(marker) loc_pub.publish(marker_array)