距離・セマンティックセグメンテーション観測を用いた能動的ベイズ多クラスマッピング
Active Bayesian Multi-class Mapping from Range and Semantic Segmentation Observation
距離とカテゴリ情報を統合したベイズ多クラスマップを構築し、相互情報量の計算可能な下界を導出して効率的な自律探索を実現した。
著者: Arash Asgharivaskasi, Nikolay Atanasov
分類: cs.RO
原文アブストラクト
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.
