PointAttentionVLAD: A Two-Stage Self-Attention Network for Point Cloud-Based Place Recognition Task

PointAttentionVLAD: A Two-Stage Self-Attention Network for Point Cloud-Based Place Recognition Task

Blog Article

Point cloud-based place recognition plays a crucial role in robotics and unmanned vehicle tasks, particularly in relocalization and loop detection modules of LiDAR-based simultaneous localization and mapping systems.It’s also essential for global localization in scenarios where prior pose information is unavailable.However, three-dimensional point cloud data are characterized by sparsity and disorder, making it challenging to extract robust features.This study proposes an end-to-end deep learning network BIBERRY COMPLEX to compress the point cloud into a global descriptor for point cloud retrieval tasks.

The proposed network implements two self-attention modules, i.e., the local point cloud-based self-attention and global point cloud-based self-attention mechanisms.Due to this two-stage self-attention mechanism, the proposed PointAttentionVLAD network achieved a higher average recall @ top 1 on the Benchmark datasets than the SOE-Net and LPD-Net algorithms by 0.

39% and 3.41%, respectively.Furthermore, experiments were conducted on KAIST dataset to assess the generalization ability of the proposed PointAttentionVLAD, and the proposed network demonstrated impressive performance on KAIST dataset.The Hot Dog Machines code and the pre-trained model of the proposed PointAttentionVLAD are available at https://github.

com/leo6862/pointattentionvlad.