基于特征关联和重用的非结构化环境神经激光雷达里程测量

IF 5.2 2区 计算机科学 Q2 ROBOTICS
Liangshu Qian, Wei Li, Yu Hu
{"title":"基于特征关联和重用的非结构化环境神经激光雷达里程测量","authors":"Liangshu Qian,&nbsp;Wei Li,&nbsp;Yu Hu","doi":"10.1002/rob.22607","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Odometry plays a crucial role in autonomous tasks of field robots, providing accurate position and orientation derived from sequential sensor observations. Odometry based on Light Detection and Ranging (LiDAR) sensors has demonstrated widespread applicability in environments with rich structured features, such as urban and indoor settings. However, for unstructured environments like scrubland and rural roads, the extraction, description, and correct matching of LiDAR features between frames become challenging. Due to the lack of flat surfaces and straight lines, the existing odometry approaches, whether using hand-crafted features such as edge and planar points or learned features through networks, will face the problem of decreased positioning accuracy and potential failure. Therefore, we propose a neural LiDAR odometry based on Trans-frame Association to extract more effective features for pose estimation in unstructured environments. The Trans-frame Association module contains a fully interactive frame Transformer and a scan-aware Swin Transformer. The former applies cross-attention to features extracted from two consecutive frames, thus enhancing the accuracy and robustness of feature correspondences by considering the contextual information. The latter restricts the attention mechanism to shift along the scan lines of LiDAR, thereby leveraging the sensor's inherent higher horizontal resolution. Our Transformer has linear complexity, which guarantees the module can meet real-time requirements. Additionally, we design a Reuse Refinement Pyramid architecture to further improve the accuracy of pose estimation by reusing multiresolution features. We conducted extensive experiments on the RELLIS-3D data set and our Matian Ridge data set collected in a representative unstructured scene. The results demonstrate that our network outperforms recent learning-based LiDAR odometry methods in terms of accuracy. The code is available at https://github.com/qlsinori/FAR-LO.</p>\n </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"3968-3985"},"PeriodicalIF":5.2000,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neural LiDAR Odometry With Feature Association and Reuse for Unstructured Environments\",\"authors\":\"Liangshu Qian,&nbsp;Wei Li,&nbsp;Yu Hu\",\"doi\":\"10.1002/rob.22607\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>Odometry plays a crucial role in autonomous tasks of field robots, providing accurate position and orientation derived from sequential sensor observations. Odometry based on Light Detection and Ranging (LiDAR) sensors has demonstrated widespread applicability in environments with rich structured features, such as urban and indoor settings. However, for unstructured environments like scrubland and rural roads, the extraction, description, and correct matching of LiDAR features between frames become challenging. Due to the lack of flat surfaces and straight lines, the existing odometry approaches, whether using hand-crafted features such as edge and planar points or learned features through networks, will face the problem of decreased positioning accuracy and potential failure. Therefore, we propose a neural LiDAR odometry based on Trans-frame Association to extract more effective features for pose estimation in unstructured environments. The Trans-frame Association module contains a fully interactive frame Transformer and a scan-aware Swin Transformer. The former applies cross-attention to features extracted from two consecutive frames, thus enhancing the accuracy and robustness of feature correspondences by considering the contextual information. The latter restricts the attention mechanism to shift along the scan lines of LiDAR, thereby leveraging the sensor's inherent higher horizontal resolution. Our Transformer has linear complexity, which guarantees the module can meet real-time requirements. Additionally, we design a Reuse Refinement Pyramid architecture to further improve the accuracy of pose estimation by reusing multiresolution features. We conducted extensive experiments on the RELLIS-3D data set and our Matian Ridge data set collected in a representative unstructured scene. The results demonstrate that our network outperforms recent learning-based LiDAR odometry methods in terms of accuracy. The code is available at https://github.com/qlsinori/FAR-LO.</p>\\n </div>\",\"PeriodicalId\":192,\"journal\":{\"name\":\"Journal of Field Robotics\",\"volume\":\"42 7\",\"pages\":\"3968-3985\"},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2025-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Field Robotics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/rob.22607\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Field Robotics","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/rob.22607","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0

摘要

测程法在野外机器人的自主任务中起着至关重要的作用,它可以从连续的传感器观测中提供准确的位置和方向。基于光探测和测距(LiDAR)传感器的里程计已经证明了在具有丰富结构特征的环境(如城市和室内环境)中的广泛适用性。然而,对于像灌木丛和乡村道路这样的非结构化环境,帧之间激光雷达特征的提取、描述和正确匹配变得具有挑战性。由于缺乏平面和直线,现有的里程测量方法,无论是使用手工制作的特征,如边缘和平面点,还是通过网络学习的特征,都将面临定位精度下降和潜在故障的问题。因此,我们提出了一种基于跨帧关联的神经网络激光雷达里程计,以提取更有效的特征,用于非结构化环境下的姿态估计。跨帧关联模块包含一个完全交互式的帧变压器和一个扫描感知的Swin变压器。前者对从两个连续帧中提取的特征进行交叉关注,从而通过考虑上下文信息提高特征对应的准确性和鲁棒性。后者限制了注意力机制沿着激光雷达的扫描线移动,从而利用传感器固有的更高水平分辨率。我们的变压器具有线性复杂性,这保证了模块可以满足实时要求。此外,我们设计了一个重用改进金字塔架构,通过重用多分辨率特征来进一步提高姿态估计的精度。我们对RELLIS-3D数据集和我们的Matian Ridge数据集进行了广泛的实验,这些数据集收集在一个具有代表性的非结构化场景中。结果表明,我们的网络在精度方面优于最近基于学习的LiDAR里程计方法。代码可在https://github.com/qlsinori/FAR-LO上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Neural LiDAR Odometry With Feature Association and Reuse for Unstructured Environments

Neural LiDAR Odometry With Feature Association and Reuse for Unstructured Environments

Odometry plays a crucial role in autonomous tasks of field robots, providing accurate position and orientation derived from sequential sensor observations. Odometry based on Light Detection and Ranging (LiDAR) sensors has demonstrated widespread applicability in environments with rich structured features, such as urban and indoor settings. However, for unstructured environments like scrubland and rural roads, the extraction, description, and correct matching of LiDAR features between frames become challenging. Due to the lack of flat surfaces and straight lines, the existing odometry approaches, whether using hand-crafted features such as edge and planar points or learned features through networks, will face the problem of decreased positioning accuracy and potential failure. Therefore, we propose a neural LiDAR odometry based on Trans-frame Association to extract more effective features for pose estimation in unstructured environments. The Trans-frame Association module contains a fully interactive frame Transformer and a scan-aware Swin Transformer. The former applies cross-attention to features extracted from two consecutive frames, thus enhancing the accuracy and robustness of feature correspondences by considering the contextual information. The latter restricts the attention mechanism to shift along the scan lines of LiDAR, thereby leveraging the sensor's inherent higher horizontal resolution. Our Transformer has linear complexity, which guarantees the module can meet real-time requirements. Additionally, we design a Reuse Refinement Pyramid architecture to further improve the accuracy of pose estimation by reusing multiresolution features. We conducted extensive experiments on the RELLIS-3D data set and our Matian Ridge data set collected in a representative unstructured scene. The results demonstrate that our network outperforms recent learning-based LiDAR odometry methods in terms of accuracy. The code is available at https://github.com/qlsinori/FAR-LO.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Field Robotics
Journal of Field Robotics 工程技术-机器人学
CiteScore
15.00
自引率
3.60%
发文量
80
审稿时长
6 months
期刊介绍: The Journal of Field Robotics seeks to promote scholarly publications dealing with the fundamentals of robotics in unstructured and dynamic environments. The Journal focuses on experimental robotics and encourages publication of work that has both theoretical and practical significance.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信