OverlapTransformer: An Efficient and Rotation-Invariant Transformer Network for LiDAR-Based Place Recognition (2203.03397v4)

Abstract: Place recognition is an important capability for autonomously navigating vehicles operating in complex environments and under changing conditions. It is a key component for tasks such as loop closing in SLAM or global localization. In this paper, we address the problem of place recognition based on 3D LiDAR scans recorded by an autonomous vehicle. We propose a novel lightweight neural network exploiting the range image representation of LiDAR sensors to achieve fast execution with less than 2 ms per frame. We design a yaw-angle-invariant architecture exploiting a transformer network, which boosts the place recognition performance of our method. We evaluate our approach on the KITTI and Ford Campus datasets. The experimental results show that our method can effectively detect loop closures compared to the state-of-the-art methods and generalizes well across different environments. To evaluate long-term place recognition performance, we provide a novel dataset containing LiDAR sequences recorded by a mobile robot in repetitive places at different times. The implementation of our method and dataset are released here: https://github.com/haomo-ai/OverlapTransformer

• The paper introduces a lightweight transformer network that efficiently processes LiDAR data for yaw-angle-invariant place recognition.
• It leverages range image encoding and multi-head self-attention to extract robust global descriptors, outperforming methods like Scan Context and PointNetVLAD.
• Empirical evaluations on KITTI, Ford Campus, and Haomo datasets confirm its superior performance in loop closure detection and real-world autonomous navigation.

Overview of OverlapTransformer - An Efficient Transformer Network for LiDAR-Based Place Recognition

The paper discusses the development of the OverlapTransformer, a novel neural network leveraging LiDAR data for improved place recognition, which is imperative in tasks such as Simultaneous Localization and Mapping (SLAM) and global localization for autonomous vehicles. The innovation proposed lies in a lightweight architecture that exploits transformer networks for efficient yaw-angle-invariant recognition from LiDAR scans.

Methodology and Architecture

Key to the solution is the application of a transformer network to process range images, a natural representation derived from 3D LiDAR scans. The transformer mechanism's attention capabilities are harnessed to extract robust global descriptors that are invariant to changes in vehicle orientation (yaw-angle). This approach effectively makes the recognition algorithm resilient to dynamic environmental effects such as rotation and lighting variability, which are common challenges in outdoor autonomous navigation.

The architecture consists of three primary components:

1. Range Image Encoder (RIE): This compresses raw LiDAR data into a reduced-dimensional feature map while retaining critical structural information.
2. Transformer Module (TM): Enhances spatial feature relationships using multi-head self-attention, contributing to the discriminative power of the output descriptors.
3. Global Descriptor Generator (GDG): A combination of multi-layer perceptrons (MLPs) and NetVLAD to produce compact global descriptors that facilitate efficient place recognition through fast and concise searchability.

Experimental Evaluation

The paper provides extensive empirical validation on multiple datasets, primarily KITTI and Ford Campus, showing the OverlapTransformer's superior performance in loop closure detection. Evaluations demonstrate that the model achieves high accuracy scores consistently outperforming existing methods such as Scan Context and PointNetVLAD. Moreover, the model generalizes well across different environments without requiring dataset-specific fine-tuning.

A unique aspect of this paper includes performance evaluation on the newly developed Haomo dataset, designed to challenge recognition systems with reverse driving sequences, longer-term place recognition, and diverse environmental conditions. The OverlapTransformer not only excels in these scenarios but also shows promising potential for practical autonomous navigation applications.

Practical and Theoretical Implications

Practically, integrating the yaw-angle-invariance feature directly into the descriptor simplifies computational requirements and enhances robustness, thereby making it highly suitable for onboard implementations in autonomous vehicles. Theoretical implications include the reaffirmation of using augmented range image representations and high-dimensional self-attention mechanisms in extricating salient environmental features, a direction worthy of further exploration in artificial intelligence and cognitive robotics research.

Future Developments

Looking ahead, the introduction of better generalization techniques, especially under transition phases between trained datasets, might advance reliability. Additionally, integrating semantic segmentation with current methods could push the boundaries further by incorporating contextual semantics into the place recognition process, potentially enhancing environment interpretation and decision-making capabilities of autonomous systems.

In conclusion, the OverlapTransformer provides a concrete step forward in the domain of LiDAR-based place recognition, with its lightweight design and robust performance setting a new standard for SLAM and autonomous localization tasks. The implications of this work are significant, not only yielding insights into effective neural network architectures for autonomous navigation but also paving the way for advanced transformative applications in robotics and AI.