Active Bayesian Multi-class Mapping from Range and Semantic Segmentation Observation
This work is significant for robots performing complex tasks that require understanding semantically meaningful objects in their environment, by extending mapping capabilities beyond binary occupancy.
This paper addresses the challenge of autonomous exploration and mapping in unknown environments by developing a Bayesian multi-class mapping algorithm that utilizes range-category measurements. The authors derive a closed-form, efficiently computable lower bound for Shannon mutual information, enabling rapid evaluation of robot trajectories for exploration and mapping.
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.