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TriLoc-NetVLAD: Enhancing Long-term Place Recognition in Orchards with a Novel LiDAR-Based Approach

文献类型： 会议论文

第一作者： Na Sun

作者： Na Sun 1 ; Zhengqiang Fan 2 ; Quan Qiu 2 ; Tao Li 3 ; Qingchun Feng 3 ; Chao Ji 4 ; Chunjiang Zhao 1 ;

作者机构： 1.College of Engineering and Technology, Southwest University,

2.College of Intelligent Science and Engineering, Beijing University of Agriculture,

3.Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences,

4.Xinjiang Academy of Agricultural and Reclamation Science,

会议名称： [ "IEEE/Robotics Society of Japan International Conference on Intelligent Robot and Systems" , "IEEE/RSJ International Conference on Intelligent Robots and Systems"]

主办单位：

页码： 16-22

摘要： Accurate long-term place recognition is crucial for agricultural robots operating in unstructured environments. However, in the challenging scene of orchard with high-frequency repetitive features, traditional LiDAR-based localization methods relying on geometric features prove to be inadequate. To address this challenge, we propose TriLoc-NetVLAD, a novel LiDAR-based long-term place recognition approach designed to handle the repetitive and ambiguous features of orchards. This approach initially fuses the point cloud density, height and spatial information to encode unordered 3D point clouds into a spatial context descriptor. Then, channel selection strategy based on descriptor's sublayer similarity between query and its corresponding positive and negative samples is proposed to amplify the differences in environmental features. Finally, we use a Triplet Network to extract local features, encompassing both high-dimensional and low-dimensional information. These local features are then cascaded through NetVLAD layer to form a global descriptor. Furthermore, we have built a cross-seasonal orchard dataset to evaluate the performance of our place recognition method. The experiment results demonstrate the advantageous localization performances of the proposed place recognition algorithm over the existing methods.

分类号： tb904.1-53`tp24

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