PIPEプランナ：地図予測を用いた経路的情報利得による屋内ロボット探索
PIPE Planner: Pathwise Information Gain with Map Predictions for Indoor Robot Exploration
屋内探索において、計画経路に沿った累積的な情報利得を地図予測と組み合わせて効率的に計算するプランナを提案し、実世界の間取りデータセットで既存手法を上回る性能を示した。
著者: Seungjae Baek, Brady Moon, Seungchan Kim, Muqing Cao, Cherie Ho, Sebastian Scherer, Jeong hwan Jeon
分類: cs.RO
原文アブストラクト
Autonomous exploration in unknown environments requires estimating the information gain of an action to guide planning decisions. While prior approaches often compute information gain at discrete waypoints, pathwise integration offers a more comprehensive estimation but is often computationally challenging or infeasible and prone to overestimation. In this work, we propose the Pathwise Information Gain with Map Prediction for Exploration (PIPE) planner, which integrates cumulative sensor coverage along planned trajectories while leveraging map prediction to mitigate overestimation. To enable efficient pathwise coverage computation, we introduce a method to efficiently calculate the expected observation mask along the planned path, significantly reducing computational overhead. We validate PIPE on real-world floorplan datasets, demonstrating its superior performance over state-of-the-art baselines. Our results highlight the benefits of integrating predictive mapping with pathwise information gain for efficient and informed exploration. Website: https://pipe-planner.github.io
