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Python中使用kitti數(shù)據(jù)集實(shí)現(xiàn)自動(dòng)駕駛(繪制出所有物體的行駛軌跡)

更新時(shí)間：2022年06月08日 09:06:53 作者：禿頭小蘇

這篇文章主要介紹了Python中使用kitti數(shù)據(jù)集實(shí)現(xiàn)自動(dòng)駕駛——繪制出所有物體的行駛軌跡,本次內(nèi)容主要是畫出kitti車的行駛的軌跡,需要的朋友可以參考下

1、利用IMU、GPS計(jì)算汽車移動(dòng)距離和旋轉(zhuǎn)角度

計(jì)算移動(dòng)距離
通過GPS計(jì)算

#定義計(jì)算GPS距離方法
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計(jì)算距離
 gps_distance += [computer_great_circle_distance(imu_data.lat,imu_data.lon,prev_imu_data.lat,prev_imu_data.lon)]

通過IMU計(jì)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']
#獲取IMU數(shù)據(jù)
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計(jì)算距離
imu_distance += [0.1*np.linalg.norm(imu_data[['vf','vl']])]

比較兩種方式計(jì)算出的距離（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()

$[外鏈圖片轉(zhuǎn)存失敗,源站可能有防盜鏈機(jī)制,建議將圖片保存下來直接上傳(img-CWY7VHDj-1640154002451)(C:\Users\WSJ\AppData\Roaming\Typora\typora-user-images\image-20211221163928106.png)]$

顯然，IMU計(jì)算的距離較為平滑。

計(jì)算旋轉(zhuǎn)角度旋轉(zhuǎn)角度的計(jì)算較為簡(jiǎn)單，我們只需要根據(jù)IMU獲取到的yaw值就可以計(jì)算（前后兩幀圖像的yaw值相減）

2、畫出kitti車的行駛軌跡

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、畫出所有車輛的軌跡

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)
#創(chuàng)建發(fā)布者        
loc_pub = rospy.Publisher('kitti_loc', MarkerArray, queue_size=10)

  #獲取距離和旋轉(zhuǎn)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)

到此這篇關(guān)于Python中使用kitti數(shù)據(jù)集實(shí)現(xiàn)自動(dòng)駕駛——繪制出所有物體的行駛軌跡的文章就介紹到這了,更多相關(guān)kitti數(shù)據(jù)集自動(dòng)駕駛內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

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