Abstract
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.
Abstract (translated)
近年来，机器人技术的进步推动了现实世界的自主，使得机器人能够执行长期和大规模任务。成功执行任务的关键组件是引入通过空间识别进行闭环闭合，有效减轻了累积姿态估计漂移。尽管计算取得了进步，为实时部署优化性能仍然具有挑战性，尤其是在资源受限的移动机器人和多机器人系统上，因为传统的基于关键帧的采样实践通常会导致保留冗余信息或忽视相关信息，因为他们依赖于固定的采样间隔或直接在三维空间而不是特征空间工作。为了应对这些担忧，我们引入了空间识别中的样本空间概念，并展示了不同采样技术如何影响查询过程和整体性能。然后，我们提出了一个基于LiDAR的紧凑表示空间中进行闭环闭合的新关键帧采样方法，重点关注降维和信息保留。这种方法适用于基于学习和手工制作的描述符，并通过在多个数据集和描述框架上的实验验证，证明了我们所提出方法的有效性，表明它可以同时最小化冗余并保留关键信息。这种方法在各种数据集上保持稳健的性能，无需进行参数调整，为各种机器人应用提供更高效、可靠的姿态识别。
URL
https://arxiv.org/abs/2410.02643
