Simultaneous Localization and Mapping (SLAM) plays an important role in robot autonomy. Reliability and efficiency are the two most valued features for applying SLAM in robot applications. In this paper, we consider achieving a reliable LiDAR-based SLAM function in computation-limited platforms, such as quadrotor UAVs based on graph-based point cloud association. First, contrary to most works selecting salient features for point cloud registration, we propose a non-conspicuous feature selection strategy for reliability and robustness purposes. Then a two-stage correspondence selection method is used to register the point cloud, which includes a KD-tree-based coarse matching followed by a graph-based matching method that uses geometric consistency to vote out incorrect correspondences. Additionally, we propose an odometry approach where the weight optimizations are guided by vote results from the aforementioned geometric consistency graph. In this way, the optimization of LiDAR odometry rapidly converges and evaluates a fairly accurate transformation resulting in the back-end module efficiently finishing the mapping task. Finally, we evaluate our proposed framework on the KITTI odometry dataset and real-world environments. Experiments show that our SLAM system achieves a comparative level or higher level of accuracy with more balanced computation efficiency compared with the mainstream LiDAR-based SLAM solutions.

翻译：同步定位与建图（SLAM）在机器人自主导航中扮演着关键角色。可靠性与效率是将SLAM应用于机器人任务时最受重视的两项特性。本文针对基于图结构的点云关联技术，探索在计算资源受限平台（如四旋翼无人机）上实现可靠性激光雷达SLAM功能的方案。首先，与多数选取显著特征进行点云配准的工作不同，我们提出一种非显著特征选择策略以增强可靠性与鲁棒性。随后，采用两阶段对应点选择方法完成点云配准：先通过KD树实现粗匹配，再基于几何一致性的图匹配方法剔除错误对应点。此外，我们提出一种里程计方法，其中权重优化由上述几何一致性投票结果引导。通过这种设计，激光雷达里程计的优化能快速收敛并评估出高精度变换矩阵，从而使得后端模块高效完成建图任务。最后，我们在KITTI里程计数据集及真实环境中对所提框架进行验证。实验表明，与主流激光雷达SLAM方案相比，本系统在计算效率更均衡的前提下，能够达到同等或更高水平的定位精度。