Open AccessBayesian place recognition based on bag of objects for intelligent vehicle localisation
Author(s) -
Wang Xianglong,
Hu Zhaozheng,
Tao Qianwen,
Huang Gang,
Mu Mengchao
Publication year - 2019
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5431
Subject(s) - artificial intelligence , computer science , bag of words model , robustness (evolution) , pattern recognition (psychology) , computer vision , histogram , feature (linguistics) , feature extraction , cognitive neuroscience of visual object recognition , image (mathematics) , biochemistry , chemistry , linguistics , philosophy , gene
Place recognition matches a corresponding image from a pre‐built image sequence, and it plays an important role in visual map‐based vehicle localisation. This study proposes a Bayesian place recognition (BPR) algorithm based on a bag of objects (BoO). The proposed BoO is superior to the classic bag of words (BoW), with a faster speed and greater robustness for scene representation. Like BoW, BoO uses object probabilities computed from the pre‐trained AlexNet to construct a histogram‐like vector called the BoO feature. A small portion of objects, named dominant objects, are computed from map data to reduce the storage requirement. The BoO feature is used to describe the scene image similarity. Owing to the excellent recognition performance of AlexNet, the BoO feature is robust to dynamic illumination environments. With BoO, the BPR model is employed to fuse appearance feature and motion clue for place recognition. Moreover, a multi‐scale localisation strategy is developed to improve the place recognition efficiency. The proposed method was tested with the collected data and the KITTI datasets. The method achieved 0.37 frame errors, taking an average of 4.7 ms for place recognition. Experimental results demonstrated that the proposed method outperforms the classic BoW and binary BoW.