Many robot applications call for autonomous exploration and mapping of unknown and unstructured environments. Information-based exploration techniques, such as Cauchy-Schwarz quadratic mutual information (CSQMI) and fast Shannon mutual information (FSMI), have successfully achieved active binary occupancy mapping with range measurements. However, as we envision robots performing complex tasks specified with semantically meaningful objects, it is necessary to capture semantic categories in the measurements, map representation, and exploration objective. This work develops a Bayesian multi-class mapping algorithm utilizing range-category measurements. We derive a closed-form efficiently computable lower bound for the Shannon mutual information between the multi-class map and the measurements. The bound allows rapid evaluation of many potential robot trajectories for autonomous exploration and mapping. We compare our method against frontier-based and FSMI exploration and apply it in a 3-D photo-realistic simulation environment.

翻译：许多机器人应用要求对未知和无结构的环境进行自主的勘探和绘图。基于信息的勘探技术,如Cauchy-Schwarz二次相互信息(CSQMI)和香农快速相互信息(FSMI),已经成功地实现了以测距法进行积极的二进制占用式绘图;然而,随着我们设想机器人执行与具有地义意义的物体有关的复杂任务,有必要在测量、地图显示和勘探目标中捕捉语义类别。这项工作开发了一种利用范围类别测量的巴伊西亚多级测绘算法。我们为多级地图和测量图之间的香农相互信息开发一种封闭式高效的可调制下线。这种测距使得能够快速评估许多潜在的机器人轨道进行自主勘探和绘图。我们将我们的方法与基于边界和FSMI的勘探方法进行比较,并将其应用于3-D摄影现实模拟环境。