MRS-VPR：マルチレゾリューションサンプリングに基づく大域的視覚位置認識手法
MRS-VPR: a multi-resolution sampling based global visual place recognition method
SeqSLAMの逐次照合を粗から細への探索とパーティクルフィルタによる大域的サンプリングで効率化し、長期参照系列中の短い軌跡を高精度に位置認識する手法を提案した。
著者: Peng Yin, Rangaprasad Arun Srivatsan, Yin Chen, Xueqian Li, Hongda Zhang, Lingyun Xu, Lu Li, Zhenzhong Jia, Jianmin Ji, Yuqing He
分類: cs.RO, cs.CV
原文アブストラクト
Place recognition and loop closure detection are challenging for long-term visual navigation tasks. SeqSLAM is considered to be one of the most successful approaches to achieving long-term localization under varying environmental conditions and changing viewpoints. It depends on a brute-force, time-consuming sequential matching method. We propose MRS-VPR, a multi-resolution, sampling-based place recognition method, which can significantly improve the matching efficiency and accuracy in sequential matching. The novelty of this method lies in the coarse-to-fine searching pipeline and a particle filter-based global sampling scheme, that can balance the matching efficiency and accuracy in the long-term navigation task. Moreover, our model works much better than SeqSLAM when the testing sequence has a much smaller scale than the reference sequence. Our experiments demonstrate that the proposed method is efficient in locating short temporary trajectories within long-term reference ones without losing accuracy compared to SeqSLAM.
