UCON: 動的環境における不確実性を考慮した履歴再関連付けナビゲーション
UCON: Uncertainty-aware Navigation with Historical Re-association in Dynamic Environments
動的環境での知覚不安定性と不確実性最適化の不一致に対処するため、点群の履歴再関連付けとカルマンフィルタによる不確実性セクターを軌道最適化に組み込んだナビゲーション手法を提案。
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著者: Bing Sun, Yue Lin, Yongsheng Yuan, Yang Liu, Dong Wang, Huchuan Lu
分類: cs.RO
原文アブストラクト
Autonomous navigation in dynamic environments is hindered by two fundamental challenges: perception instability and uncertainty-optimization mismatch. The former leads to identity switches and unreliable motion estimation, while the latter prevents principled incorporation of motion uncertainty into trajectory optimization. To address these challenges, we propose UCON, an uncertainty-aware navigation algorithm in dynamic environments. For perception instability, we present a point-level historical re-association mechanism that leverages historical point cloud fragments to recover lost targets while maintaining identity continuity. Subsequently, a Kalman filter is employed to provide anisotropic motion state estimation and covariance propagation. To resolve the uncertainty-optimization mismatch, we transform predicted states and their covariances into uncertainty sectors, which are embedded as differentiable cost terms within a trajectory optimization framework. This achieves consistent uncertainty-aware dynamic obstacle avoidance while maintaining smoothness and feasibility. Extensive simulations and real-world experiments demonstrate that, while maintaining high computational efficiency, UCON achieves superior perception stability and robust navigation performance in dynamic environments compared to state-of-the-art methods. The code will be open-sourced to facilitate further research.