CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data (2302.01665v2)

Abstract: LiDAR-based place recognition (LPR) is one of the most crucial components of autonomous vehicles to identify previously visited places in GPS-denied environments. Most existing LPR methods use mundane representations of the input point cloud without considering different views, which may not fully exploit the information from LiDAR sensors. In this paper, we propose a cross-view transformer-based network, dubbed CVTNet, to fuse the range image views (RIVs) and bird's eye views (BEVs) generated from the LiDAR data. It extracts correlations within the views themselves using intra-transformers and between the two different views using inter-transformers. Based on that, our proposed CVTNet generates a yaw-angle-invariant global descriptor for each laser scan end-to-end online and retrieves previously seen places by descriptor matching between the current query scan and the pre-built database. We evaluate our approach on three datasets collected with different sensor setups and environmental conditions. The experimental results show that our method outperforms the state-of-the-art LPR methods with strong robustness to viewpoint changes and long-time spans. Furthermore, our approach has a good real-time performance that can run faster than the typical LiDAR frame rate. The implementation of our method is released as open source at: https://github.com/BIT-MJY/CVTNet.

• The paper introduces CVTNet, which fuses range and bird's eye views from LiDAR via intra- and inter-transformers to achieve robust place recognition.
• It leverages a dual-transformer architecture to extract and align multi-view features, outperforming state-of-the-art methods on multiple challenging datasets.
• The method operates in real time at approximately 30 Hz, demonstrating robust recognition and computational efficiency for autonomous driving.

Overview of "CVTNet: A Cross-View Transformer Network for LiDAR-Based Place Recognition in Autonomous Driving Environments"

The paper presents "CVTNet," a Cross-View Transformer Network designed to enhance place recognition in autonomous vehicles by leveraging multi-view representations of LiDAR data. Traditional LiDAR-based place recognition (LPR) methodologies typically utilize singular, mundane data representations, potentially overlooking critical information present in LiDAR scans. CVTNet addresses this gap by fusing range image views (RIVs) and bird's eye views (BEVs), both derived from LiDAR data, to form more robust, viewpoint-invariant global descriptors.

Core Components and Methodology:

1. Multi-View Fusion: CVTNet integrates RIVs and BEVs using intra- and inter-transformers to analyze both intra-view correlations and cross-view interactions. This dual-view integration allows the system to generate descriptors that are invariant to changes in the yaw angle, ensuring robustness across different environmental conditions and sensor setups.
2. Transformer Network Architecture: The network architecture employs an intra-transformer to extract features within individual views and an inter-transformer to align and fuse these features across views. This architecture enhances the ability to discern nuanced relationships within and between the different types of sensor data, crucial for accurate place recognition tasks.
3. Real-Time Capabilities and Evaluation: CVTNet's design ensures that it can process and generate descriptors faster than the typical LiDAR frame rate, making it suitable for real-time applications in autonomous driving scenarios. The paper reports outperforming state-of-the-art methods across several datasets, indicating CVTNet's superior recognition accuracy and computational efficiency.

Key Experimental Findings:

• Performance Metrics:

CVTNet demonstrated significant improvements in average recall (AR) metrics compared to baseline methods across multiple challenging datasets, including the NCLT dataset, KITTI sequences, and a self-recorded autonomous driving dataset. It showed superior loop closure detection and place recognition capabilities, even under varying viewpoint conditions.

• Robustness to Viewpoint Changes:

The architecture's innovative incorporation of yaw-angle invariance through aligned feature encoding and transformer fusion contributes to its robustness against different driving directions and rotations, a common occurrence in real-world autonomous driving scenarios.

• Runtime Efficiency:

The CVTNet's processing time is optimized to operate at approximately 30 Hz, thus outpacing common LiDAR frame rates and affirming its applicability in time-sensitive environments like autonomous vehicles.

Practical and Theoretical Implications:

The progress demonstrated by CVTNet in LiDAR-based place recognition suggests several implications:

• Theoretical Advancements:

The fusion of dual-view LiDAR data through the transformer framework sets a precedent for multi-modal data integration in robotics, potentially influencing future research across similarly structured environmental perception tasks.

• Practical Applications:

The enhancement in robustness and accuracy could lead to more reliable autonomous navigation solutions, contributing to increased safety and efficiency in real-world deployments.

Future Directions:

While CVTNet advances the state-of-the-art in LPR, there is potential for further research to extend its application:

• Exploring the integration of additional sensor modalities (e.g., cameras or radar) into the transformer framework.
• Investigating scalability and performance in more complex or dynamically changing environments beyond the datasets evaluated.
• Assessing the adaptability of the architecture for other robotics and automated systems beyond autonomous vehicles.

In conclusion, CVTNet marks a significant stride in LiDAR-based autonomous navigation, emphasizing the transformative potential of cross-view, transformer-based architectures in processing complex environmental data. Its deployment in real-world scenarios can help bridge existing gaps in autonomous driving technologies, fostering more resilient and dependable systems.