论文标题
seqot：一种空间变压器网络，用于使用顺序LIDAR数据的位置识别
SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data
论文作者
论文摘要
位置识别是自动驾驶汽车实现循环结束或全球本地化的重要组成部分。在本文中，我们根据机上激光雷达传感器获得的顺序3D激光扫描解决了位置识别问题。我们提出了一个名为SEQOT的基于变压器的网络，以利用由LIDAR数据生成的顺序范围图像提供的时间和空间信息。它使用多尺度变压器以端到端的方式为每一个LiDAR范围图像生成一个全局描述符。在在线操作期间，我们的SEQOT通过在当前查询序列和地图中存储的描述符之间匹配此类描述符来找到类似的位置。我们在不同类型的不同环境中使用不同类型的LIDAR传感器收集的四个数据集上评估了我们的方法。实验结果表明，我们的方法的表现优于最先进的激光雷达识别方法，并在不同环境中概括了。此外，我们的方法比传感器的帧速率更快地在线运行。我们方法的实现以开源为开源：https：//github.com/bit-mjy/seqot。
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.
