Visual-Inertial SLAM for Unstructured Outdoor Environments: Benchmarking the Benefits and Computational Costs of Loop Closing

IF 5.2 2区 计算机科学 Q2 ROBOTICS
Fabian Schmidt, Constantin Blessing, Markus Enzweiler, Abhinav Valada
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Abstract

Simultaneous localization and mapping (SLAM) is essential for mobile robotics, enabling autonomous navigation in dynamic, unstructured outdoor environments without relying on external positioning systems. These environments pose significant challenges due to variable lighting, weather conditions, and complex terrain. Visual-Inertial SLAM has emerged as a promising solution for robust localization under such conditions. This paper benchmarks several open-source visual-Inertial SLAM systems, including traditional methods (ORB-SLAM3, VINS-Fusion, OpenVINS, Kimera, and SVO Pro) and learning-based approaches (HFNet-SLAM, AirSLAM), to evaluate their performance in unstructured natural outdoor settings. We focus on the impact of loop closing on localization accuracy and computational demands, providing a comprehensive analysis of these systems' effectiveness in real-world environments and especially their application to embedded systems in outdoor robotics. Our contributions further include an assessment of varying frame rates on localization accuracy and computational load. The findings highlight the importance of loop closing in improving localization accuracy while managing computational resources efficiently, offering valuable insights for optimizing Visual-Inertial SLAM systems for practical outdoor applications in mobile robotics. The data set and the benchmark code are available under https://github.com/iis-esslingen/vi-slam_lc_benchmark.

Abstract Image

用于非结构化室外环境的视觉惯性SLAM:闭环的基准效益和计算成本
同时定位和绘图(SLAM)对于移动机器人来说至关重要,它可以在动态、非结构化的室外环境中实现自主导航,而无需依赖外部定位系统。由于多变的光照、天气条件和复杂的地形,这些环境构成了巨大的挑战。在这种情况下,视觉惯性SLAM已经成为一种很有前途的鲁棒定位解决方案。本文对几种开源视觉惯性SLAM系统进行了基准测试,包括传统方法(ORB-SLAM3、VINS-Fusion、OpenVINS、Kimera和SVO Pro)和基于学习的方法(HFNet-SLAM、AirSLAM),以评估它们在非结构化自然户外环境中的性能。我们专注于闭环关闭对定位精度和计算需求的影响,提供了这些系统在现实环境中的有效性的全面分析,特别是它们在户外机器人嵌入式系统中的应用。我们的贡献还包括评估不同帧率对定位精度和计算负载的影响。研究结果强调了闭环在提高定位精度的同时有效管理计算资源的重要性,为优化视觉惯性SLAM系统在移动机器人中的实际户外应用提供了有价值的见解。数据集和基准代码可从https://github.com/iis-esslingen/vi-slam_lc_benchmark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。
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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.
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