確率的セマンティックセグメンテーションに基づく視覚的不確かさを考慮した動作計画
Vision-Based Uncertainty-Aware Motion Planning based on Probabilistic Semantic Segmentation
深層セマンティックセグメンテーションのアンサンブルと大規模データ拡張で確率的な占有情報を推定し、シナリオベースの経路計画と速度調整により不確実環境での安全なロボット動作を実現する手法を提案した。
著者: Ralf Römer, Armin Lederer, Samuel Tesfazgi, Sandra Hirche
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
For safe operation, a robot must be able to avoid collisions in uncertain environments. Existing approaches for motion planning under uncertainties often assume parametric obstacle representations and Gaussian uncertainty, which can be inaccurate. While visual perception can deliver a more accurate representation of the environment, its use for safe motion planning is limited by the inherent miscalibration of neural networks and the challenge of obtaining adequate datasets. To address these limitations, we propose to employ ensembles of deep semantic segmentation networks trained with massively augmented datasets to ensure reliable probabilistic occupancy information. To avoid conservatism during motion planning, we directly employ the probabilistic perception in a scenario-based path planning approach. A velocity scheduling scheme is applied to the path to ensure a safe motion despite tracking inaccuracies. We demonstrate the effectiveness of the massive data augmentation in combination with deep ensembles and the proposed scenario-based planning approach in comparisons to state-of-the-art methods and validate our framework in an experiment with a human hand as an obstacle.
