通过稀疏可行假设采样和可靠的分批多阶段推理解决被绑架的机器人问题
Tackling the Kidnapped Robot Problem via Sparse Feasible Hypothesis Sampling and Reliable Batched Multi-Stage Inference
AI总结：
本文提出了一种被动的2D全局重定位框架，通过单个LiDAR扫描和占用网格地图在机器人静止时高效可靠地估计全局姿态，从而提高移动机器人的长期自主性。该框架将全局重定位问题转化为非凸问题，并通过多假设方案与分批多阶段推理和早期终止平衡完整性和效率。
摘要：
本文针对被绑架的机器人问题（KRP），即在已知地图中重新定位机器人时，没有先验姿态估计或在SLAM初始化时的定位丢失问题。为此，提出了一种被动的2D全局重定位框架。该框架在机器人静止时，通过单个LiDAR扫描和占用网格地图高效可靠地估计全局姿态，从而提高移动机器人的长期自主性。所提出的框架将全局重定位问题转化为非凸问题，并通过多假设方案与分批多阶段推理和早期终止来解决，平衡完整性和效率。快速探索随机树（RRT）在可通行性约束下，渐近覆盖可达空间以生成稀疏、均匀分布的可行位置假设，从根本上减少采样空间。假设首先通过所提出的扫描均方差（SMAD）进行排序，这是一种粗略的光束误差水平度量，通过优先处理高可能性的候选者来实现早期终止。SMAD计算优化以适应有限的扫描测量。提出的翻译亲和度扫描到地图对齐度量（TAM）用于在假设位置可靠地选择方向，并准确评估最终的全局姿态，以减轻由于稀疏假设引起的翻译不确定性以及非全景LiDAR扫描和环境变化导致的传统似然场度量的退化。在资源受限的移动机器人上的真实世界实验表明，所提出的框架在成功率、在测量不确定性下的鲁棒性和计算效率方面均表现优异。
英文摘要：
This paper addresses the Kidnapped Robot Problem (KRP), a core localization challenge of relocalizing a robot in a known map without prior pose estimate upon localization loss or at SLAM initialization. For this purpose, a passive 2-D global relocalization framework is proposed. It estimates the global pose efficiently and reliably from a single LiDAR scan and an occupancy grid map while the robot remains stationary, thereby enhancing the long-term autonomy of mobile robots. The proposed framework casts global relocalization as a non-convex problem and solves it via the multi-hypothesis scheme with batched multi-stage inference and early termination, balancing completeness and efficiency. The Rapidly-exploring Random Tree (RRT), under traversability constraints, asymptotically covers the reachable space to generate sparse, uniformly distributed feasible positional hypotheses, fundamentally reducing the sampling space. The hypotheses are preliminarily ordered by the proposed Scan Mean Absolute Difference (SMAD), a coarse beam-error level metric that facilitates the early termination by prioritizing high-likelihood candidates. The SMAD computation is optimized for limited scan measurements. The Translation-Affinity Scan-to-Map Alignment Metric (TAM) is proposed for reliable orientation selection at hypothesized positions and accurate final global pose evaluation to mitigate degradation in conventional likelihood-field metrics under translational uncertainty induced by sparse hypotheses, as well as non-panoramic LiDAR scan and environmental changes. Real-world experiments on a resource-constrained mobile robot with non-panoramic LiDAR scans show that the proposed framework achieves competitive performance in success rate, robustness under measurement uncertainty, and computational efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
