视觉和激光雷达里程计紧耦合的SLAM算法

SLAM Algorithm with Tight Coupling of Vision and LiDAR Odometer

针对视觉和激光耦合simultaneous localization and mapping(SLAM)中存在的视觉特征丢失、雷达闭环轨迹矢量漂移和高程位姿偏差问题,提出一种通过扫描上下文回环检测的紧密耦合视觉和激光雷达SLAM方法。采用基于SIFT、ORB特征点检测器的视觉里程计解决特征点丢失和匹配失败问题;通过雷达里程计融合视觉里程计帧间估计消除雷达点云畸变和大幅度漂移;通过扫描上下文进行回环检测,引入因子图优化里程计矢量漂移,消除回环检测失败问题。在多个KITTI数据集中对所提算法进行验证,并与经典算法进行对比,实验结果表明,该算法具有高稳定性、较强鲁棒性、低漂移和高精度。

To address the problems of visual feature loss,radar closed-loop trajectory vector drift,and elevation pose deviation in vision and laser coupled simultaneous localization and mapping(SLAM),a close coupled vision and lidar SLAM method based on scanning context loop detection is proposed.A visual odometer based on SIFT and the ORB feature point detector is used to solve the problem of feature point loss and matching failure.A radar odometer eliminates the distortion and large drift of the radar point cloud by fusing the inter-frame estimation of the visual odometer.Loopback detection is performed by scanning context,and the vector drift of the odometer is optimized by introducing the factor graph to eliminate loopback detection failure.The proposed algorithm is verified on several KITTI datasets and compared with classical algorithms.The experimental results show that the algorithm exhibits high stability,strong robustness,low drift,and high accuracy.