Typical LiDAR SLAM architectures feature a front-end for odometry estimation and a back-end for refining and optimizing the trajectory and map, commonly through loop closures. However, loop closure detection in large-scale missions presents significant computational challenges due to the need to identify, verify, and process numerous candidate pairs for pose graph optimization. Keyframe sampling bridges the front-end and back-end by selecting frames for storing and processing during global optimization. This article proposes an online keyframe sampling approach that constructs the pose graph using the most impactful keyframes for loop closure. We introduce the Minimal Subset Approach (MSA), which optimizes two key objectives: redundancy minimization and information preservation, implemented within a sliding window framework. By operating in the feature space rather than 3-D space, MSA efficiently reduces redundant keyframes while retaining essential information. In sum, evaluations on diverse public datasets show that the proposed approach outperforms naive methods in reducing false positive rates in place recognition, while delivering superior ATE and RPE in metric localization, without the need for manual parameter tuning. Additionally, MSA demonstrates efficiency and scalability by reducing memory usage and computational overhead during loop closure detection and pose graph optimization.

翻译：典型的LiDAR SLAM架构包含用于里程计估计的前端和用于通过回环检测等方式优化轨迹与地图的后端。然而，在大规模任务中，回环检测因需要识别、验证并处理大量用于位姿图优化的候选对而面临显著的计算挑战。关键帧采样通过在全球优化过程中选择用于存储和处理的帧，连接了前端与后端。本文提出一种在线关键帧采样方法，该方法利用对回环检测最具影响力的关键帧构建位姿图。我们引入了最小子集方法（MSA），该方法在滑动窗口框架内优化两个关键目标：冗余最小化与信息保留。通过在特征空间而非三维空间中操作，MSA能有效减少冗余关键帧，同时保留关键信息。总体而言，在多种公开数据集上的评估表明，所提方法在降低地点识别中的误报率方面优于朴素方法，同时在无需手动参数调优的情况下，在度量定位中实现了更优的绝对轨迹误差（ATE）与相对位姿误差（RPE）。此外，MSA通过减少回环检测与位姿图优化过程中的内存占用和计算开销，展现了其高效性与可扩展性。