This paper mainly studies the information-theoretic exploration in an environmental representation with dense belief, considering pose uncertainty for range sensing robots. Previous works concern more about active mapping/exploration with known poses or utilize inaccurate information metrics, resulting in imbalanced exploration. This motivates us to extend the confidence-rich mutual information (CRMI) with measurable pose uncertainty. Specifically, we propose a Rao-Blackwellized particle filter-based confidence-rich localization and mapping (RBPF-CRLM) scheme with a new closed-form weighting method. We further compute 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 in unstructured environments.
翻译:本文主要研究在具有浓厚信念的环境代表中的信息理论探索,考虑给测距机器人带来不确定性。以前的工作更关注以已知外形进行积极测绘/勘探或使用不准确的信息度量,从而导致勘探不平衡。这促使我们扩大具有可测量的不确定性的具有信心的相互信息(CRMI ) 。具体地说,我们建议采用一种新的封闭式加权法,采用以高信任度、高信任度和高信任度过滤粒子定位和绘图法(RBPF-CRLM ) 。我们进一步以更准确的近似方式计算加权微粒的不确定的CRMI(UCRMI ) 。模拟和实验评估显示拟议方法在无结构环境中的本地化准确性和探索性。