论文标题
基于机器人勘探的粒子过滤器的置信度丰富的定位和映射
Confidence-rich Localization and Mapping based on Particle Filter for Robotic Exploration
论文作者
论文摘要
本文主要研究富含置信度图（CRM）中范围传感机器人的定位和映射，然后将其扩展以提供信息理论探索的完整状态估计。关于主动同时定位，映射和探索的大多数著作总是假定已知的机器人姿势或利用不准确的信息指标来近似姿势不确定性，从而导致未知环境中的勘探性能和效率不平衡。这激发了我们以可测量的姿势不确定性扩展富含信心的互信息（CRMI）。具体而言，我们为CRM提出了一种基于Rao-Blackwellized粒子过滤器的定位和映射方案（RBPF-CLAM），然后我们开发了一种新的封闭形式的加权方法来提高本地化准确性而无需扫描匹配。我们通过更准确的近似值进一步推导了不确定的CRMI（UCRMI）。模拟和实验评估显示了所提出方法的定位准确性和勘探性能。
This paper mainly studies the localization and mapping of range sensing robots in the confidence-rich map (CRM) and then extends it to provide a full state estimate for information-theoretic exploration. Most previous works about active simultaneous localization and mapping and exploration always assumed the known robot poses or utilized inaccurate information metrics to approximate pose uncertainty, resulting in imbalanced exploration performance and efficiency in the unknown environment. This inspires us to extend the confidence-rich mutual information (CRMI) with measurable pose uncertainty. Specifically, we propose a Rao-Blackwellized particle filter-based localization and mapping scheme (RBPF-CLAM) for CRM, then we develop a new closed-form weighting method to improve the localization accuracy without scan matching. We further derive the uncertain CRMI (UCRMI) with the weighted particles by a more accurate approximation. Simulations and experimental evaluations show the localization accuracy and exploration performance of the proposed methods.
