モンテカルロサンプリングに基づく高速ロボット自己位置推定法
AFast Robot Localization Method Based on Monte Carlo Sampling
4方向の距離センサのみを用い、方位情報なしで迷路環境における移動ロボットの位置をオンライン粒子フィルタで推定する手法を提案し、類似領域が多い迷路でも高精度に位置推定できることをシミュレーションで示した。
著者: He Zhaoqing, Hang Li, Xian Pu, Li Wang
分類: cs.RO
原文アブストラクト
Localization is an essential capability of intelligent robots. However, in real-world constrained environments, robots often cannot rely on external localization systems. Therefore, this paper proposes an online particle-filter localization method based on Monte Carlo sampling. The method is applied to the localization of a mobile robot in a maze environment and requires only four range sensors mounted on the front, rear, left, and right sides of the vehicle, without the need for any heading information. Simulation experiments demonstrate that even in a maze where local regions are highly similar, the vehicle can still achieve relatively accurate localization, thereby providing a fast and effective approach for robot localization in complex environments.