The similarity metric in Loop closure detection(lcd) is still considered in an old fashioned way, i.e. to pre-define a fixed distance function, leading to a limited performance. This paper proposes a general framework...
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The similarity metric in Loop closure detection(lcd) is still considered in an old fashioned way, i.e. to pre-define a fixed distance function, leading to a limited performance. This paper proposes a general framework named lrn-lcd, i.e. a Lightweight relation network for lcd, which combines the feature extraction module and similarity metric module into a simple and lightweight network. The lrn-lcd, an end-to-end framework, can learn a non-linear deep similarity metric to detect loop closures from different scenes. Moreover, the lrn-lcd supports image sequences as input to speed up the similarity metric in real-time applications. Extensive experiments on several open datasets illustrate that lrn-lcd is more robust to strong condition variations and viewpoint variations than the mainstream methods.
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