  Embodied agents require robust navigation systems to operate in unstructured
environments, making the robustness of Simultaneous Localization and Mapping
(SLAM) models critical to embodied agent autonomy. While real-world datasets
are invaluable, simulation-based benchmarks offer a scalable approach for
robustness evaluations. However, the creation of a challenging and controllable
noisy world with diverse perturbations remains 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. The pipeline comprises a comprehensive taxonomy of sensor and
motion perturbations for embodied multi-modal (specifically RGB-D) sensing,
categorized by their sources and propagation order, allowing for procedural
composition. We also provide a toolbox for synthesizing these perturbations,
enabling the transformation of clean environments into challenging noisy
simulations. Utilizing the pipeline, we instantiate the large-scale
Noisy-Replica benchmark, which includes diverse perturbation types, to evaluate
the risk tolerance of existing advanced RGB-D SLAM models. Our extensive
analysis uncovers the susceptibilities of both neural (NeRF and Gaussian
Splatting -based) and non-neural SLAM models to disturbances, despite their
demonstrated accuracy in standard benchmarks. Our code is publicly available at
https://github.com/Xiaohao-Xu/SLAM-under-Perturbation.
