Light-LOAM：一种基于图匹配的轻量级激光雷达里程计与建图方法
Light-LOAM: A Lightweight LiDAR Odometry and Mapping based on Graph-Matching
AI总结：
针对计算受限平台的激光雷达SLAM需求，提出轻量级框架Light-LOAM，采用非显著特征选择、两阶段图匹配配准及投票引导的权重优化，在保证精度的同时提升计算效率。
摘要：
同时定位与建图（SLAM）在机器人自主能力中发挥着重要作用，可靠性与效率是SLAM应用于机器人场景时最受重视的两大特性。本文研究在计算资源受限的平台（如四旋翼无人机）上，基于图的点云关联实现可靠的激光雷达SLAM功能。首先，与多数选取显著特征做点云配准的工作不同，我们提出一种非显著特征选择策略，以提升可靠性与鲁棒性。随后采用两阶段对应点选择方法配准点云：先进行基于KD-tree的粗匹配，再使用基于图的匹配方法，借助几何一致性剔除错误对应点。此外，我们提出一种里程计方法，其权重优化由上述几何一致性图的投票结果引导，使激光雷达里程计的优化快速收敛，得到精度较高的变换结果，从而让后端模块高效完成建图任务。最后，我们在KITTI里程计数据集和真实环境中评估了所提框架。实验表明，与主流激光雷达SLAM方案相比，本文的SLAM系统精度达到相当或更高水平，同时计算效率更均衡。
英文摘要：
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.
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