论文标题
通过重新识别在线视觉位置识别
Online Visual Place Recognition via Saliency Re-identification
论文作者
论文摘要
作为视觉同时定位和映射的重要组成部分（SLAM），位置识别对于机器人导航和自动驾驶至关重要。现有方法通常将视觉位置识别为特征匹配，对于许多计算能力有限的机器人应用，例如自动驾驶和清洁机器人，计算在计算上昂贵。受到人类总是通过记住比其他人更具吸引力或有趣的地标，这一事实的启发，我们将视觉位置识别为显着性重新识别。同时，我们建议在频域中同时进行显着性检测和重新识别，其中所有操作都是元素的。实验表明，我们提出的方法可实现竞争精度，并且比基于最新功能的方法更高。提出的方法是开源的，可在https://github.com/wh200720041/srlcd.git上找到。
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.
