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Journal Articles IEEE Robotics and Automation Letters Year : 2022

Binary Graph Descriptor for Robust Relocalization on Heterogeneous Data

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Abstract

In this paper, we propose a novel binary graph descriptor to improve loop detection for visual SLAM systems. Our contribution is twofold: i) a graph embedding technique for generating binary descriptors which conserve both spatial and histogram information extracted from images; ii) a generic mean of combining multiple layers of heterogeneous data into the proposed binary graph descriptor, coupled with a matching and geometric checking method. We also introduce an implementation of our descriptor into an incremental Bag-of-Words (iBoW) structure that improves efficiency and scalability, and propose a method to interpret Deep Neural Network (DNN) results. We evaluate our system on synthetic and real datasets across different lighting and seasonal conditions. The proposed method outperforms state-of-the-art loop detection frameworks in terms of relocalization precision and computational performance, as well as displays high robustness against cross-condition datasets.
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Dates and versions

hal-03506034 , version 1 (10-01-2022)

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Xi Wang, Marc Christie, Eric Marchand. Binary Graph Descriptor for Robust Relocalization on Heterogeneous Data. IEEE Robotics and Automation Letters, 2022, 7 (2), pp.2008 - 2015. ⟨10.1109/LRA.2022.3142854⟩. ⟨hal-03506034⟩
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