顕著性の再同定によるオンライン視覚的場所認識
Online Visual Place Recognition via Saliency Re-identification
人間が目立つランドマークで場所を覚えることに着想を得て、視覚的場所認識を顕著性の再同定として定式化し、周波数領域で検出と照合を高速に行う手法を提案した。
著者: Han Wang, Chen Wang, Lihua Xie
分類: cs.CV, cs.RO
原文アブストラクト
As an essential component of visual simultaneous localization and mapping (SLAM), place recognition is crucial for robot navigation and autonomous driving. Existing methods often formulate visual place recognition as feature matching, which is computationally expensive for many robotic applications with limited computing power, e.g., autonomous driving and cleaning robot. Inspired by the fact that human beings always recognize a place by remembering salient regions or landmarks that are more attractive or interesting than others, we formulate visual place recognition as saliency re-identification. In the meanwhile, we propose to perform both saliency detection and re-identification in frequency domain, in which all operations become element-wise. The experiments show that our proposed method achieves competitive accuracy and much higher speed than the state-of-the-art feature-based methods. The proposed method is open-sourced and available at https://github.com/wh200720041/SRLCD.git.
