Place Recognition of Large-Scale Unstructured Orchards With Attention Score Maps

Abstract

The availability of autonomous orchard robots could alleviate the conflict caused by rising labor costs and labor shortages. The essential technical requirements are autonomous localization and mapping which rely on place recognition to explore data associations. This letter presents a novel LiDAR-based place recognition algorithm for unstructured and large-scale orchards. Concretely, we propose a discriminative global representation, spatial binary pattern (SBP), that encodes three-dimensional (3D) spatial distributed scan into an eight-bit binary pattern. In addition, an efficient two-stage hierarchical re-identification process is proposed. The attention score map is introduced for task-relevant features and preliminary candidates retrieval. The overlap re-identification is used to align a pair of descriptors to confirm the final loop closure index. Experiments on orchard and public datasets have been conducted to evaluate the performance of the proposed method, our method achieves a higher recall rate and localization accuracy. Moreover, experiments on the long-term outdoor dataset KITTI further demonstrate the generality.