面向自动泊车的可变滑动窗口环视SLAM算法

Variable sliding window panoramic SLAM algorithmfor automatic parking

针对车辆在自动泊车场景中遇到减速带或坑洼地带导致的地图偏移问题,以及动态环境多变导致的系统实时性变差、精度降低的问题,提出了一种面向自动泊车的可变滑动窗口环视SLAM算法。首先,使用IMU对采集的环视图像进行实时姿态校准,提升建图的准确性;其次,结合多传感器融合优势,融合IMU和里程计数据对车辆位姿进行估计;最后,通过可变滑动窗口算法加速后端优化,提升系统的实时性和精度。仿真测试结果表明,该方法解决了减速带或坑洼地带的地图偏移问题,且在特征稀疏环境下,效率和实时性分别提升了31.35%和25.06%。实车试验结果表明,该方法可以达到平均误差0.039 m的定位精度,为泊车提供了安全保障。

Aiming at the problem of map offset caused by deceleration zone or pothole zone encountered by vehicles in automatic parking scenarios,and the problem of poor real-time performance and low accuracy of the system caused by changing dynamic environment,a variable sliding window look-around SLAM algorithm for automatic parking is proposed.Firstly,IMU is used to calibrate the real-time attitude of the collected panoramic image to improve the accuracy of mapping.Secondly,combined with the advantages of multi-sensor fusion,the IMU and odometer data are fused to estimate the vehicle pose.Finally,the variable sliding window algorithm is used to accelerate the back-end optimization and improve the real-time performance and accuracy of the system.The simulation test results show that the method solves the problem of map offset in the deceleration zone or the pit zone,and the efficiency and real-time performance are improved by 31.35%and 25.06%respectively in the sparse feature environment.The real vehicle test results show that the method can achieve the positioning accuracy with an average error of 0.039 m,which provides safety guarantee for parking.