Customizable Perturbation Synthesis for Robust SLAM Benchmarking
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916123301117952 |
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| author | Xu, Xiaohao Zhang, Tianyi Wang, Sibo Li, Xiang Chen, Yongqi Li, Ye Raj, Bhiksha Johnson-Roberson, Matthew Huang, Xiaonan |
| author_facet | Xu, Xiaohao Zhang, Tianyi Wang, Sibo Li, Xiang Chen, Yongqi Li, Ye Raj, Bhiksha Johnson-Roberson, Matthew Huang, Xiaonan |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_08125 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Customizable Perturbation Synthesis for Robust SLAM Benchmarking Xu, Xiaohao Zhang, Tianyi Wang, Sibo Li, Xiang Chen, Yongqi Li, Ye Raj, Bhiksha Johnson-Roberson, Matthew Huang, Xiaonan Robotics Artificial Intelligence Computer Vision and Pattern Recognition Multimedia 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/. |
| title | Customizable Perturbation Synthesis for Robust SLAM Benchmarking |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2402.08125 |