BEVPlace++: Fast, Robust, and Lightweight LiDAR Global Localization for Unmanned Ground Vehicles

Abstract: This article introduces BEVPlace++, a novel, fast, and robust LiDAR global localization method for unmanned ground vehicles. It uses lightweight convolutional neural networks (CNNs) on Bird's Eye View (BEV) image-like representations of LiDAR data to achieve accurate global localization through place recognition, followed by 3-DoF pose estimation. Our detailed analyses reveal an interesting fact that CNNs are inherently effective at extracting distinctive features from LiDAR BEV images. Remarkably, keypoints of two BEV images with large translations can be effectively matched using CNN-extracted features. Building on this insight, we design a Rotation Equivariant Module (REM) to obtain distinctive features while enhancing robustness to rotational changes. A Rotation Equivariant and Invariant Network (REIN) is then developed by cascading REM and a descriptor generator, NetVLAD, to sequentially generate rotation equivariant local features and rotation invariant global descriptors. The global descriptors are used first to achieve robust place recognition, and then local features are used for accurate pose estimation. \revise{Experimental results on seven public datasets and our UGV platform demonstrate that BEVPlace++, even when trained on a small dataset (3000 frames of KITTI) only with place labels, generalizes well to unseen environments, performs consistently across different days and years, and adapts to various types of LiDAR scanners.} BEVPlace++ achieves state-of-the-art performance in multiple tasks, including place recognition, loop closure detection, and global localization. Additionally, BEVPlace++ is lightweight, runs in real-time, and does not require accurate pose supervision, making it highly convenient for deployment. \revise{The source codes are publicly available at https://github.com/zjuluolun/BEVPlace2.

• The paper introduces BEVPlace++, a system that uses CNN-extracted BEV features and a rotation equivariant module to achieve precise 3-DoF global localization.
• It demonstrates state-of-the-art performance in place recognition, loop closure detection, and overall localization across multiple public datasets.
• The method operates in real time with efficient deployment, showing strong generalization across various environments and LiDAR scanner types.

An Overview of BEVPlace++: Enhanced LiDAR Global Localization for Unmanned Ground Vehicles

The paper introduces BEVPlace++, an advanced LiDAR global localization approach designed specifically for unmanned ground vehicles (UGVs). This work emphasizes the integration of fast, robust, and lightweight convolutional neural networks (CNNs) with Bird's Eye View (BEV) image-like representations. The core innovation centers around utilizing CNNs' inherent capabilities to extract distinct features from LiDAR BEV images to achieve precise global localization through place recognition, followed by 3-DoF (Degrees of Freedom) pose estimation.

Key Technical Contributions

1. Insight on CNN Feature Extraction: The research highlights how CNNs can effectively identify and match keypoints across BEV images with significant translational variations. This capability forms the basis for further developments in the system's robustness to environmental changes.
2. Design of Rotation Equivariant Module (REM): The creation of REM allows the model to maintain robustness against rotational changes. By extracting rotation equivariant features, this module enhances the model's capability to manage diverse viewpoint shifts efficiently.
3. Development of Rotation Equivariant and Invariant Network (REIN): By integrating REM with NetVLAD, the system sequentially generates both rotation equivariant local features and rotation invariant global descriptors. This hierarchical feature extraction process enables robust place recognition and, consequently, precise pose estimation.
4. Generalization Across Environments: BEVPlace++ demonstrates impressive adaptability, successfully generalizing to unfamiliar environments and various LiDAR scanner types without specific retraining. Particularly notable is its performance across multiple public datasets, maintaining consistent operation over different temporal and spatial settings.
5. Efficiency and Deployment Convenience: The method achieves real-time localization capabilities and operates without requiring accurate pose supervision, beneficial for practical deployment in real-world applications.

Performance and Implications

The study reports state-of-the-art performance in various subtasks of global localization, including place recognition, loop closure detection, and overall global localization. Through comprehensive experiments across five distinct public datasets, the effectiveness of BEVPlace++ in diverse conditions is well-demonstrated.

The reported numerical results are particularly strong, indicating high localization accuracy with minimal computational overhead. The results confirm that BEVPlace++ is not only effective in controlled, training environments but also robust in real-world, dynamic settings.

Broader Impact and Future Directions

The development of BEVPlace++ pushes the boundaries of autonomous vehicle operation through improved perception and localization technologies. By enabling more precise and reliable localization, BEVPlace++ contributes to the advancement of autonomous systems capable of functioning efficiently under a wide array of conditions.

Looking forward, the extension of BEVPlace++ can involve enhancing its performance in highly dynamic urban environments and further optimizing its computational efficiency. Additionally, exploring integration with other sensor modalities could provide more comprehensive situational awareness, paving the way for more sophisticated multi-modal localization systems.

In conclusion, BEVPlace++ represents a significant step forward in LiDAR-based localization for unmanned ground vehicles. Its contributions to both theoretical understanding and practical implementation offer a solid foundation for future advancements in autonomous navigation technology.