With the universal application of camera in intelligent vehicles, visual place recognition has become a major problem in intelligent vehicle localization. The traditional solution is to make visual description of place images using hand-crafted feature for matching places, but this description method is not very good for extreme variability, especially for seasonal transformation. In this paper, we propose a new method based on convolutional neural network (CNN), by putting images into the pre-trained network model to get automatically learned image descriptors, and through some operations of pooling, fusion and binarization to optimize them, then the similarity result of place recognition is presented with the Hamming distance of the place sequence. In the experimental part, we compare our method with some state-of-the-art algorithms, FABMAP, ABLE-M and SeqSLAM, to illustrate its advantages. The experimental results show that our method based on CNN achieves better performance than other methods on the representative public datasets.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Visual Place Recognition in Long-term and Large-scale Environment based on CNN Feature


    Contributors:
    Zhu, Jianliang (author) / Ai, Yunfeng (author) / Tian, Bin (author) / Cao, Dongpu (author) / Scherer, Sebastian (author)


    Publication date :

    2018-06-01


    Size :

    2385056 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    All-environment visual place recognition with SMART

    Pepperell, Edward / Corke, Peter I. / Milford, Michael J. | IEEE | 2014


    Long-Term Knowledge Distillation of Visual Place Classifiers

    Tomoe, Hiroki / Kanji, Tanaka | IEEE | 2019


    CMM: LiDAR-Visual Fusion with Cross-Modality Module for Large-Scale Place Recognition

    Li, Bin / Lu, Fan / Chen, Guang et al. | SAE Technical Papers | 2023


    Trajectory-Based Place-Recognition for Efficient Large Scale Localization

    Lynen, S. / Bosse, M. / Siegwart, R. | British Library Online Contents | 2017


    ImageNet Large Scale Visual Recognition Challenge

    Russakovsky, O. / Deng, J. / Su, H. et al. | British Library Online Contents | 2015