STAR-Filter: ノイズ環境における星形集合フィルタリングによる効率的な凸自由空間近似
STAR-Filter: Efficient Convex Free-Space Approximation via Starshaped Set Filtering in Noisy Environments
衝突回避空間の凸近似を高速化するため、星形集合構築をフィルタとして用いる軽量フレームワークSTAR-Filterを提案。冗長計算を削減し、ノイズや大規模データに対して頑健なポリトープ生成を実現した。
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著者: Yuwei Wu, Yichen Zhao, Dexter Ong, Vijay Kumar
分類: cs.RO
原文アブストラクト
Approximating collision-free space is fundamental to robot planning in complex environments. Convex geometric representations, such as polytopes and ellipsoids, are widely employed due to their structural properties, which can be easily integrated with convex optimization. Iterative optimization-based inflation methods can generate large volume polytopes in cluttered environments, but their efficiency degrades as the obstacle set becomes more complex or when sensor data are noisy. These methods are also sensitive to initialization and often rely on accurate geometric models. In this paper, we propose the STAR-Filter, a lightweight framework that employs starshaped set construction as a fast filter for convex region generation in collision-free space. By identifying obstacle points as active supporting constraints, the proposed method significantly reduces redundant computation while preserving feasibility and robustness to sensor noise. We provide theoretical and numerical analyses that characterize the structural properties of the starshaped set and proposed pipeline in environments of varying complexity. Simulation results show that the proposed framework achieves the lowest computation time and reduces conservativeness in polytope generation for real-world noisy and large-scale data. We demonstrate the effectiveness of the framework for Safe Flight Corridor (SFC) generation and agile quadrotor planning in noisy environments.
