Shen Bingke, Xie Wenming, Peng Xiaodong, Qiao Xiaoning, Guo Zhiyuan
National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2024 Nov 26;24(23):7546. doi: 10.3390/s24237546.
Current lidar-inertial SLAM algorithms mainly rely on the geometric features of the lidar for point cloud alignment. The issue of incorrect feature association arises because the matching process is susceptible to influences such as dynamic objects, occlusion, and environmental changes. To address this issue, we present a lidar-inertial SLAM system based on the LIO-SAM framework, combining semantic and geometric constraints for association optimization and keyframe selection. Specifically, we mitigate the impact of erroneous matching points on pose estimation by comparing the consistency of normal vectors in the surrounding region. Additionally, we incorporate semantic information to establish semantic constraints, further enhancing matching accuracy. Furthermore, we propose an adaptive selection strategy based on semantic differences between frames to improve the reliability of keyframe generation. Experimental results on the KITTI dataset indicate that, compared to other systems, the accuracy of the pose estimation has significantly improved.
当前的激光雷达惯性同步定位与地图构建（SLAM）算法主要依靠激光雷达的几何特征进行点云对齐。由于匹配过程容易受到动态物体、遮挡和环境变化等影响，会出现特征关联错误的问题。为了解决这个问题，我们提出了一种基于LIO - SAM框架的激光雷达惯性SLAM系统，结合语义和几何约束进行关联优化和关键帧选择。具体来说，我们通过比较周围区域法向量的一致性来减轻错误匹配点对姿态估计的影响。此外，我们纳入语义信息以建立语义约束，进一步提高匹配精度。此外，我们提出了一种基于帧间语义差异的自适应选择策略，以提高关键帧生成的可靠性。在KITTI数据集上的实验结果表明，与其他系统相比，姿态估计的准确性有了显著提高。
