ロバストな運動平均化による複数グリッドマップの同時統合
Simultaneous merging multiple grid maps using the robust motion averaging
ペアごとの地図統合で相対運動を推定し、グラフベースのサンプリングで信頼できるものだけを選んで運動平均化により全グローバル運動を復元し、複数のグリッドマップを同時に統合する手法を提案した。
著者: Zutao Jiang, Jihua Zhu, Yaochen Li, Zhongyu Li, Huimin Lu
分類: cs.AI, cs.RO
原文アブストラクト
Mapping in the GPS-denied environment is an important and challenging task in the field of robotics. In the large environment, mapping can be significantly accelerated by multiple robots exploring different parts of the environment. Accordingly, a key problem is how to integrate these local maps built by different robots into a single global map. In this paper, we propose an approach for simultaneous merging of multiple grid maps by the robust motion averaging. The main idea of this approach is to recover all global motions for map merging from a set of relative motions. Therefore, it firstly adopts the pair-wise map merging method to estimate relative motions for grid map pairs. To obtain as many reliable relative motions as possible, a graph-based sampling scheme is utilized to efficiently remove unreliable relative motions obtained from the pair-wise map merging. Subsequently, the accurate global motions can be recovered from the set of reliable relative motions by the motion averaging. Experimental results carried on real robot data sets demonstrate that proposed approach can achieve simultaneous merging of multiple grid maps with good performances.
