Semantic OcTree Mapping and Shannon Mutual Information Computation for Robot Exploration

Autonomous robot operation in unstructured and unknown environments requires efficient techniques for mapping and exploration using streaming range and visual observations. 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 presents Semantic octree mapping and Shannon Mutual Information (SSMI) computation for robot exploration. We develop a Bayesian multi-class mapping algorithm based on the OcTree data structure, where each voxel maintains a categorical distribution over object classes. Furthermore, we derive a closed-form efficiently-computable lower bound for the Shannon mutual information between the multi-class OctoMap and a set of range-category measurements using Run-Length Encoding (RLE) of the sensor rays. 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 variety of simulated and real-world experiments.