面向非结构化室外环境的视觉-惯性SLAM：回环检测的益处与计算成本基准评估
Visual-Inertial SLAM for Unstructured Outdoor Environments: Benchmarking the Benefits and Computational Costs of Loop Closing
AI总结：
本文基准评估了多种开源视觉-惯性SLAM系统在非结构化室外环境中的性能，重点分析回环检测对定位精度与计算成本的影响，并考察帧率的作用，为移动机器人嵌入式应用提供优化依据。
摘要：
同时定位与建图（SLAM）对于移动机器人至关重要，使其能够在动态、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。由于光照变化、天气条件和复杂地形，这些环境带来了重大挑战。视觉-惯性SLAM已成为在此类条件下实现鲁棒定位的一种有前景的解决方案。本文对多个开源视觉-惯性SLAM系统进行了基准评估，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室外环境中的性能。我们重点关注回环检测对定位精度和计算需求的影响，对这些系统在真实环境中的有效性，尤其是它们在室外机器人嵌入式系统中的应用，进行了全面分析。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。研究结果凸显了回环检测在提高定位精度的同时高效管理计算资源方面的重要性，为优化面向移动机器人实际室外应用的视觉-惯性SLAM系统提供了有价值的见解。数据集和基准代码可在https://github.com/iis-esslingen/vi-slam_lc_benchmark获取。
英文摘要：
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 dataset and the benchmark code are available under https://github.com/iis-esslingen/vi-slam_lc_benchmark.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
