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

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.