Semantic OcTree Mapping and Shannon Mutual Information Computation for Robot Exploration

Abstract

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 and fast Shannon mutual information, have successfully achieved active binary occupancy mapping with range measurements. However, as we envision robots performing complex tasks specified with semantically meaningful concepts, it is necessary to capture semantics in the measurements, map representation, and exploration objective. This work presents semantic octree mapping and Shannon mutual information computation for robot exploration. We develop a Bayesian multiclass mapping algorithm based on an octree data structure, where each voxel maintains a categorical distribution over semantic classes. We derive a closed-form efficiently computable lower bound of the Shannon mutual information between a multiclass octomap and a set of range-category measurements using semantic run-length encoding of the sensor rays. The bound allows rapid evaluation of many potential robot trajectories for autonomous exploration and mapping. We compare our method against state-of-the-art exploration techniques and apply it in a variety of simulated and real-world experiments.