SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data (2209.07951v1)

Abstract: Place recognition is an important component for autonomous vehicles to achieve loop closing or global localization. In this paper, we tackle the problem of place recognition based on sequential 3D LiDAR scans obtained by an onboard LiDAR sensor. We propose a transformer-based network named SeqOT to exploit the temporal and spatial information provided by sequential range images generated from the LiDAR data. It uses multi-scale transformers to generate a global descriptor for each sequence of LiDAR range images in an end-to-end fashion. During online operation, our SeqOT finds similar places by matching such descriptors between the current query sequence and those stored in the map. We evaluate our approach on four datasets collected with different types of LiDAR sensors in different environments. The experimental results show that our method outperforms the state-of-the-art LiDAR-based place recognition methods and generalizes well across different environments. Furthermore, our method operates online faster than the frame rate of the sensor. The implementation of our method is released as open source at: https://github.com/BIT-MJY/SeqOT.

Overview of SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data

Place recognition continues to be an essential function in the field of autonomous vehicles, laying the foundation for practices such as loop closure and global localization. The paper "SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data" introduces a novel methodology leveraging sequential LiDAR scans to enhance place recognition capabilities. This paper presents the SeqOT model, an end-to-end transformer-based network that integrates spatial and temporal information effectively to generate global descriptors for place recognition.

SeqOT Methodology

The SeqOT model aligns with the growing trend of utilizing temporal sequences to improve recognition robustness over various environmental conditions. By applying multi-scale transformers, SeqOT creates a compact global descriptor for each LiDAR sequence. This descriptor is then used to match locations in a map, striking a balance between local and global feature representation.

1. Single-Scan Module: A distinctive characteristic of SeqOT is in its single-scan module, responsible for initial feature extraction from individual LiDAR scans. This module utilizes a convolutional architecture to capture spatial features, which are further refined by a transformer network, hence optimizing both the computational cost and feature quality.
2. Multi-Scan Module: Crucial to the SeqOT’s architecture, this module processes the extracted spatial features from consecutive scans capturing temporal information. This strategy enables the model to represent long-range spatial dependencies within the LiDAR data, intertwining spatial context with time-sequential observations.
3. Global Descriptor Generation: Leveraging the GeM pooling technique, SeqOT amalgamates sub-descriptors from consecutive scans into a single global descriptor. This descriptor is invariant to drive direction, boasting robustness in varying orientations and conditions, an essential aspect for real-world applications in dynamic environmental setups.

Performance and Generalization

Performance evaluations of SeqOT demonstrate its superiority over existing single-scan and sequence-based methods. On datasets that feature diverse environments and sensor setups, SeqOT consistently outperforms other methods in recognizing places, notably maintaining a high recall rate across substantial temporal spans without the necessity for fine-tuning. This indicates SeqOT's applicability in heterogeneous environments where generalization capabilities are paramount.

Additionally, SeqOT's efficiency is validated through its capability to operate online faster than the sensor's frame rate, signifying its potential for real-time applications.

Future Directions

SeqOT exemplifies a significant step towards more adaptive recognition systems that can effectively leverage temporal information from sequential data. Future research could explore further integration with other sensory data such as visual or inertial inputs, potentially enhancing robustness and accuracy. Investigations into more sophisticated fusion strategies and attention mechanisms could refine feature selection, ultimately leading to more comprehensive environmental understanding and improved autonomy in robotics.

Overall, the "SeqOT" model showcases the innovative application of transformer architectures in 3D place recognition, making it a notable contribution to the development of autonomous systems. Its open-source availability will likely encourage further development and adaptation of transformer network applications in similar domains.