遮蔽環境の自律ナビゲーションのための学習ベース3D占有予測
Learning-based 3D Occupancy Prediction for Autonomous Navigation in Occluded Environments
深層学習で環境の文脈情報から遮蔽空間の障害物分布を予測し、未知空間を安全かつ積極的に計画できるようにして移動ロボットのナビゲーション性能を向上させた研究。
著者: Lizi Wang, Hongkai Ye, Qianhao Wang, Yuman Gao, Chao Xu, Fei Gao
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
In autonomous navigation of mobile robots, sensors suffer from massive occlusion in cluttered environments, leaving significant amount of space unknown during planning. In practice, treating the unknown space in optimistic or pessimistic ways both set limitations on planning performance, thus aggressiveness and safety cannot be satisfied at the same time. However, humans can infer the exact shape of the obstacles from only partial observation and generate non-conservative trajectories that avoid possible collisions in occluded space. Mimicking human behavior, in this paper, we propose a method based on deep neural network to predict occupancy distribution of unknown space reliably. Specifically, the proposed method utilizes contextual information of environments and learns from prior knowledge to predict obstacle distributions in occluded space. We use unlabeled and no-ground-truth data to train our network and successfully apply it to real-time navigation in unseen environments without any refinement. Results show that our method leverages the performance of a kinodynamic planner by improving security with no reduction of speed in clustered environments.
