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

Electrolytic 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 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 Heartrate Electrode 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 code and the pre-trained model of the proposed PointAttentionVLAD are available at https://github.com/leo6862/pointattentionvlad.