This letter focuses on information-theoretic active mapping and exploration with beam-based range sensing robots. Traditional works based on hand-engineered inverse sensor model (ISM) mapping or kernel inference methods lead to imbalanced accuracy and efficiency. This motivates us to propose a new approach to compute mutual information more accurately, based on the continuous belief distribution over the occupancy map and called confidence-rich mutual information (CRMI). Specifically, we explicitly model the measurement dependencies between grid cells within the same measurement cone at each time step and derive the CRMI for each cell on all beams by introducing a more general beam-based sensor cause model (SCM), rather than the customized ISM. The time efficiency for CRMI mapping allows for online implementation as well. Extensive simulations and experiments show the desired exploratory behavior to unexplored and obscured regions for CRMI-based robot controllers in the unstructured and cluttered scene, even in large scale environment.

中文翻译：

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CRMI：用于信息理论映射的富置信互信息

这封信重点介绍了基于光束的距离传感机器人的信息论主动映射和探索。基于手工设计的逆传感器模型 (ISM) 映射或核推理方法的传统工作导致准确性和效率不平衡。这促使我们提出一种新的方法来更准确地计算互信息，基于占用图上的连续置信度分布，称为可信互信息 (CRMI)。具体来说，我们在每个时间步明确地对同一测量锥内的网格单元之间的测量依赖性进行建模，并通过引入更通用的基于波束的传感器原因模型 (SCM)，而不是定制的 ISM，导出所有波束上每个单元的 CRMI . CRMI 映射的时间效率也允许在线实施。大量的模拟和实验表明，即使在大规模环境中，基于 CRMI 的机器人控制器在非结构化和杂乱场景中对未探索和模糊区域的探索行为也是如此。