Loop-closure detection (LCD), which aims to recognize a previously visited location, is a crucial component of the simultaneous localization and mapping system. In this paper, a novel appearance-based LCD method is presented. In particular, we propose a simple yet surprisingly useful feature matching algorithm for real-time geometrical verification of candidate loop-closures, termed as local relative orientation matching (LRO). It aims to efficiently establish reliable feature correspondences based on preserving local topological structures between the query image and candidate frame. To effectively retrieve candidate loop closures, we introduce the aggregated selective match kernel framework into the LCD task, which can effectively represent images and reduce the quantization noise of the traditional bag-of-words framework. In addition, the SuperPoint neural network is employed to extract reliable interest points and feature descriptors. Extensive experimental results demonstrate that our LRO can significantly improve the LCD performance, and the proposed overall LCD method can achieve much better performance over the current state-of-the-art on six publicly available datasets.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Loop-Closure Detection Using Local Relative Orientation Matching


    Contributors:
    Ma, Jiayi (author) / Ye, Xinyu (author) / Zhou, Huabing (author) / Mei, Xiaoguang (author) / Fan, Fan (author)


    Publication date :

    2022-07-01


    Size :

    4052004 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Semantic Loop Closure Detection for Intelligent Vehicles Using Panoramas

    Xiao, Dingwen / Li, Sirui / Xuanyuan, Zhe | IEEE | 2023



    PLSAV: Parallel loop searching and verifying for loop closure detection

    Zhe Yang / Yun Pan / Lei Deng et al. | DOAJ | 2021

    Free access

    Circle detection based on orientation matching

    Ceccarelli, M. / Petrosino, A. / Laccetti, G. | IEEE | 2001