使用kitti数据集实现自动驾驶——绘制出所有物体的行驶轨迹

本次内容主要是上周内容的延续，主要画出kitti车的行驶的轨迹

同样的，我们先来看看最终实现的效果：
https://gitee.com/wsj-create/autodrive/blob/master/自动驾驶视频2.mp4

 
接下来就进入一步步的编码环节。。。

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()
    

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)
            

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