LiDAR odometry can achieve accurate vehicle pose estimation for short driving range or in small-scale environments, but for long driving range or in large-scale environments, the accuracy deteriorates as a result of cumulative estimation errors. This drawback necessitates the inclusion of loop closure detection in a SLAM framework to suppress the adverse effects of cumulative errors. To improve the accuracy of pose estimation, we propose a new LiDAR-based SLAM method which uses F-LOAM as LiDAR odometry, Scan Context for loop closure detection, and GTSAM for global optimization. In our approach, an adaptive distance threshold (instead of a fixed threshold) is employed for loop closure detection, which achieves more accurate loop closure detection results. Besides, a feature-based matching method is used in our approach to compute vehicle pose transformations between loop closure point cloud pairs, instead of using the raw point cloud obtained by the LiDAR sensor, which significantly reduces the computation time. The KITTI dataset is used for verifications of our method, and the experimental results demonstrate that the proposed method outperforms typical LiDAR odometry/SLAM methods in the literature. Our code is made publicly available for the benefit of the community.

翻译：摘要：激光雷达里程计在短距离行驶或小规模环境中可实现精确的车辆位姿估计，但在长距离行驶或大规模环境中，由于累积估计误差，其精度会显著下降。这一缺陷要求SLAM框架中必须包含回环检测模块以抑制累积误差的不利影响。为提升位姿估计精度，我们提出一种新型基于激光雷达的SLAM方法，该方法采用F-LOAM作为激光雷达里程计，使用扫描上下文进行回环检测，并利用GTSAM实现全局优化。在该方法中，我们采用自适应距离阈值（而非固定阈值）进行回环检测，从而获得更精确的回环检测结果。此外，我们提出基于特征的点云匹配方法计算回环点云对间的车辆位姿变换，而非直接使用激光雷达传感器获取的原始点云数据，这显著降低了计算耗时。通过KITTI数据集验证，实验结果表明所提方法优于文献中典型的激光雷达里程计/SLAM方法。为惠及学界，我们已将代码开源。