Recent advances in robotics are pushing real-world autonomy, enabling robots to perform long-term and large-scale missions. A crucial component for successful missions is the incorporation of loop closures through place recognition, which effectively mitigates accumulated pose estimation drift. Despite computational advancements, optimizing performance for real-time deployment remains challenging, especially in resource-constrained mobile robots and multi-robot systems since, conventional keyframe sampling practices in place recognition often result in retaining redundant information or overlooking relevant data, as they rely on fixed sampling intervals or work directly in the 3D space instead of the feature space. To address these concerns, we introduce the concept of sample space in place recognition and demonstrate how different sampling techniques affect the query process and overall performance. We then present a novel keyframe sampling approach for LiDAR-based place recognition, which focuses on redundancy minimization and information preservation in the hyper-dimensional descriptor space. This approach is applicable to both learning-based and handcrafted descriptors, and through the experimental validation across multiple datasets and descriptor frameworks, we demonstrate the effectiveness of our proposed method, showing it can jointly minimize redundancy and preserve essential information in real-time. The proposed approach maintains robust performance across various datasets without requiring parameter tuning, contributing to more efficient and reliable place recognition for a wide range of robotic applications.

翻译：机器人学的最新进展正在推动现实世界自主性的发展，使机器人能够执行长期和大规模的任务。成功执行任务的一个关键组成部分是通过地点识别实现闭环检测，这能有效缓解累积的位姿估计漂移。尽管计算技术不断进步，但在资源受限的移动机器人和多机器人系统中，优化性能以实现实时部署仍然具有挑战性。这是因为传统的地点识别关键帧采样方法通常依赖于固定的采样间隔或直接在三维空间而非特征空间中进行操作，这往往导致保留冗余信息或忽略相关数据。为解决这些问题，我们引入了地点识别中的采样空间概念，并展示了不同采样技术如何影响查询过程和整体性能。接着，我们提出了一种新颖的基于LiDAR的地点识别关键帧采样方法，该方法专注于在高维描述子空间中实现冗余最小化和信息保留。这种方法适用于基于学习的描述子和手工设计的描述子。通过在多个数据集和描述子框架上进行实验验证，我们证明了所提方法的有效性，表明它能够实时联合最小化冗余并保留关键信息。所提出的方法在不同数据集上均保持稳健性能，且无需参数调整，有助于为广泛的机器人应用实现更高效、更可靠的地点识别。