意味的ガイド付き探索：画像空間ウェイポイントサンプリングによる非構造環境の探索
SGE: Semantically-Guided Exploration for Unstructured Environments via Image-Space Waypoint Sampling
地上車両向けの探索フレームワークで、ピクセル単位の意味セグメンテーションをウェイポイント選択と経路最適化に統合し、地形の走行可能性や障害物、関心対象を考慮した意味的効用関数で探索目標を評価する。シミュレーションと実環境で性能を検証した。
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著者: Christopher Tatsch, Yu Gu
分類: cs.RO, cs.AI
原文アブストラクト
This work introduces Semantically-Guided Exploration (SGE), a modular exploration framework for ground vehicles that integrates pixel-level semantic segmentation into sampling-based waypoint selection and receding-horizon route optimization. Unlike conventional geometric exploration methods, SGE evaluates candidate exploration goals directly in the image space using a semantic-aware utility function that accounts for terrain traversability, obstacle proximity, objects of interest, and depth-based exploration reward. Sampled waypoints are projected into 3D and ordered through a real-time Traveling Salesman Problem (TSP) formulation, enabling receding-horizon goal selection. To address real-world navigation uncertainty, the framework introduces mechanisms, including temporary taboo regions to handle navigation failures and a graph-based relocation strategy for efficient backtracking across explored areas. We evaluate SGE in standardized simulation benchmarks against state-of-the-art exploration planners and demonstrate competitive performance in volumetric coverage, while enabling semantic task biasing that cannot be achieved by purely geometric methods. The framework is further validated through real-world experiments using multiple robotic platforms in indoor campus buildings and in limestone and coal mines. Results show consistent performance and adaptability across platforms and domains.
