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arXiv 2402.08125cs.ROcs.AIcs.CVcs.MM

面向鲁棒SLAM基准测试的可定制扰动合成

Customizable Perturbation Synthesis for Robust SLAM Benchmarking

University of Michigan（密歇根大学）；Carnegie Mellon University（卡内基梅隆大学）

Xiaohao Xu, Tianyi Zhang, Sibo Wang, Xiang Li, Yongqi Chen, Ye Li, Bhiksha Raj, Matthew Johnson-Roberson, Xiaonan Huang

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40 pages
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AI总结：

提出可定制噪声数据合成流水线，构建Robust-SLAM基准测试，评估多模态SLAM模型对扰动的鲁棒性，揭示现有模型在真实世界扰动下的脆弱性。

摘要：

鲁棒性是机器人在非结构化环境中成功部署的关键因素，特别是在同时定位与地图构建（SLAM）领域。与现实世界数据收集相比，基于仿真的基准测试已成为一种高度可扩展的鲁棒性评估方法。然而，构建一个具有挑战性且可控的包含多样扰动的噪声世界仍相对缺乏探索。为此，我们提出一种新颖的、可定制的噪声数据合成流水线，旨在评估多模态SLAM模型对各种扰动的抵抗能力。该流水线包含可定制的硬件配置、软件组件和受扰环境。具体而言，我们引入了全面的扰动分类法以及扰动合成工具箱，允许将干净的仿真转换为具有挑战性的噪声环境。利用该流水线，我们实例化了Robust-SLAM基准测试，该基准包含多样的扰动类型，用于评估现有先进多模态SLAM模型的风险容忍度。我们的广泛分析揭示了现有SLAM模型对真实世界扰动的的易感性，尽管它们们在标准基准测试中表现出准确性。我们的扰动合成工具箱、SLAM鲁棒性评估流水线和Robust-SLAM基准测试将在https://github.com/Xiaohao-Xu/SLAM-under-Perturbation/公开发布。

英文摘要：

Robustness is a crucial factor for the successful deployment of robots in unstructured environments, particularly in the domain of Simultaneous Localization and Mapping (SLAM). Simulation-based benchmarks have emerged as a highly scalable approach for robustness evaluation compared to real-world data collection. However, crafting a challenging and controllable noisy world with diverse perturbations remains relatively under-explored. To this end, we propose a novel, customizable pipeline for noisy data synthesis, aimed at assessing the resilience of multi-modal SLAM models against various perturbations. This pipeline incorporates customizable hardware setups, software components, and perturbed environments. In particular, we introduce comprehensive perturbation taxonomy along with a perturbation composition toolbox, allowing the transformation of clean simulations into challenging noisy environments. Utilizing the pipeline, we instantiate the Robust-SLAM benchmark, which includes diverse perturbation types, to evaluate the risk tolerance of existing advanced multi-modal SLAM models. Our extensive analysis uncovers the susceptibilities of existing SLAM models to real-world disturbance, despite their demonstrated accuracy in standard benchmarks. Our perturbation synthesis toolbox, SLAM robustness evaluation pipeline, and Robust-SLAM benchmark will be made publicly available at https://github.com/Xiaohao-Xu/SLAM-under-Perturbation/.

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。

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