论文标题
优化的SC-F-LOAM：使用扫描上下文优化的快速发光镜和映射
Optimized SC-F-LOAM: Optimized Fast LiDAR Odometry and Mapping Using Scan Context
论文作者
论文摘要
激光射道可以在短练习范围或小型环境中实现准确的车辆姿势估计，但是对于长练习范围或在大规模环境中，由于累积估计误差，准确性恶化。这种缺点需要将循环闭合检测纳入猛击框架，以抑制累积错误的不利影响。为了提高姿势估计的准确性，我们提出了一种新的基于激光雷达的SLAM方法，该方法将F-LOAM用作LIDAR射测，扫描环境进行环闭合检测，而GTSAM进行全局优化。在我们的方法中，使用自适应距离阈值（而不是固定阈值）进行环闭合检测，从而实现了更准确的环路闭合检测结果。此外，在我们的方法中使用了一种基于功能的匹配方法来计算循环闭合点云对之间的车辆姿势转换，而不是使用LIDAR传感器获得的原始点云，从而大大减少了计算时间。 KITTI数据集用于验证我们的方法，实验结果表明，所提出的方法在文献中优于典型的激光探光仪/猛击方法。我们的代码可公开用于社区的利益。
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.
