不確実性を考慮したリアルタイム体積・意味マッピングによる探索
Real-Time Volumetric-Semantic Exploration and Mapping: An Uncertainty-Aware Approach
セマンティックセグメンテーションと不確実性を考慮した情報獲得報酬を用い、ドローンの次最良視点計画で対象物を優先しつつ効率的に探索・マッピングする枠組みを提案した。
著者: Rui Pimentel de Figueiredo, Jonas le Fevre Sejersen, Jakob Grimm Hansen, Martim Brandão, Erdal Kayacan
分類: cs.RO
原文アブストラクト
In this work we propose a holistic framework for autonomous aerial inspection tasks, using semantically-aware, yet, computationally efficient planning and mapping algorithms. The system leverages state-of-the-art receding horizon exploration techniques for next-best-view (NBV) planning with geometric and semantic segmentation information provided by state-of-the-art deep convolutional neural networks (DCNNs), with the goal of enriching environment representations. The contributions of this article are threefold, first we propose an efficient sensor observation model, and a reward function that encodes the expected information gains from the observations taken from specific view points. Second, we extend the reward function to incorporate not only geometric but also semantic probabilistic information, provided by a DCNN for semantic segmentation that operates in real-time. The incorporation of semantic information in the environment representation allows biasing exploration towards specific objects, while ignoring task-irrelevant ones during planning. Finally, we employ our approaches in an autonomous drone shipyard inspection task. A set of simulations in realistic scenarios demonstrate the efficacy and efficiency of the proposed framework when compared with the state-of-the-art.
