Xiaohao Xu
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Tianyi Zhang*
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Sibo Wang*
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Xiang Li
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Yongqi Chen
Ye Li
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Bhiksha Raj
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Matthew Johnson-Roberson
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Xiaonan Huang
UMich Robotics · CMU Robotics · CMU ECE
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Generative models, such as Sora, can simulate very COOL videos but fail to capture the physics and dynamics of our Real World.
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We highlight the uniqueness and merits of physics-aware Noisy World simulators, and propose a customizable perturbation synthesis pipeline that can transform a Clean World to a Noisy World in a controllable manner.
- Noisy data synthesis pipeline for SLAM evaluation under perturbation.
- (a) Given the customizable robot system and global trajectory, (b) the local trajectory of each sensor can be generated via the physics engine. (c) Subsequently, the trajectory perturbation composer introduces deviations to simulate locomotion perturbations (d) Following this, the render combines sensor configurations, perturbed local trajectories, and 3D scene models to generate sensor streams. (e) Finally, the sensor perturbation composer introduces corruptions to the clean sensor streams, (f) resulting in perturbed data for SLAM robustness benchmarking
- Robustness is a crucial factor for the successful deployment of robots in unstructured environments, particularly in the domain of Simultaneous Localization and Mapping (SLAM).
- We propose a novel, customizable pipeline for noisy data synthesis, aimed at assessing the resilience of multi-modal SLAM models against various perturbations.
- 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.
- Perturbation: Evaluate the SLAM model’s robustness under mixed perturbations and more diverse perturbation types.
- Synthesis: How to generate more realistic perturabtions and environments for more reliable SLAM simulation?
- SLAM Model: Extend the robustness evaluation to SLAM models with more diverse input modality types, e.g., LiDAR and sonar.
- Robustness Enhancement: Can you propose a more robust SLAM model that can survive more severe and diverse perturbations?
- Beyond SLAM: The evaluation can be easily extended to 3D reconstruction and other robotic navigation tasks.
- (Please refer to the paper for more details 😄)
Please cite our paper if you find this repo useful! 💛 💙 💛 💙
@article{xu2024customizable,
title={Customizable Perturbation Synthesis for Robust SLAM Benchmarking},
author={Xu, Xiaohao and Zhang, Tianyi and Wang, Sibo and Li, Xiang and Chen, Yongqi and Li, Ye and Raj, Bhiksha and Johnson-Roberson, Matthew and Huang, Xiaonan},
journal={arXiv preprint arXiv:2402.08125},
year={2024}
}If you have any question about this project, please feel free to contact xiaohaox@umich.edu