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

翻译：位置识别是自主导航车辆在复杂环境和变化条件下运行的重要能力，也是SLAM中闭环检测或全局定位等任务的关键组成部分。本文针对基于自主车辆记录的3D激光雷达扫描的位置识别问题，提出了一种新颖的轻量级神经网络。该方法利用激光雷达传感器的距离图像表示，实现了每帧不到2毫秒的快速执行。我们设计了一种利用Transformer网络的偏航角不变架构，提升了位置识别的性能。在KITTI和Ford Campus数据集上的实验结果表明，与最先进的方法相比，我们的方法能有效检测闭环，并在不同环境下具有良好的泛化能力。为评估长期位置识别性能，我们提供了一个新数据集，其中包含移动机器人在重复地点不同时间记录的激光雷达序列。本文方法及数据集的实现已发布在：https://github.com/haomo-ai/OverlapTransformer