论文标题
Overlaptransformer：用于基于激光雷达的位置识别的高效且旋转不变的变压器网络
OverlapTransformer: An Efficient and Rotation-Invariant Transformer Network for LiDAR-Based Place Recognition
论文作者
论文摘要
位置识别是自主在复杂环境和变化条件下操作的车辆的重要功能。它是诸如SLAM或全局本地化循环结束之类的任务的关键组件。在本文中，我们根据自动驾驶汽车记录的基于3D激光扫描的位置识别问题。我们提出了一个新型的轻型神经网络，利用LiDAR传感器的范围图像表示，以每帧少于2 ms的快速执行。我们设计了利用变压器网络的偏航 - 角不变体系结构，从而提高了我们方法的位置识别性能。我们在Kitti和Ford校园数据集上评估了我们的方法。实验结果表明，与最先进的方法相比，我们的方法可以有效地检测回路封闭，并在不同环境中概述。为了评估长期的位置识别性能，我们提供了一个新颖的数据集，其中包含由移动机器人在不同时间的移动机器人记录的雷达序列。我们的方法和数据集的实现在此处发布：https：//github.com/haomo-ai/overlaptransformer
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
