# Failure or Drift? Evaluating Monocular SLAM under Synthetic and Real-World Corruptions

arXiv: https://arxiv.org/abs/2608.30690 · HTML: https://mlsanity.com/paper/2608.30690/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 44 references that are in the map.
- First posted on arXiv 31 Aug 2026.

## Builds on (most cited first, 30 of 31)

- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2108.10869/) (2021)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020)
- [Gaussian Splatting SLAM](https://mlsanity.com/paper/2312.06741/) (2023)
- [SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM](https://mlsanity.com/paper/2312.02126/) (2023)
- [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://mlsanity.com/paper/1907.07484/) (2019)
- [GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting](https://mlsanity.com/paper/2311.11700/) (2023)
- [MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors](https://mlsanity.com/paper/2412.12392/) (2024)
- [Deep Patch Visual Odometry](https://mlsanity.com/paper/2208.04726/) (2022)
- [GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction](https://mlsanity.com/paper/2309.02436/) (2023)
- [3D Common Corruptions and Data Augmentation](https://mlsanity.com/paper/2203.01441/) (2022)
- [SLAM3R: Real-Time Dense Scene Reconstruction from Monocular RGB Videos](https://mlsanity.com/paper/2412.09401/) (2024)
- [4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving](https://mlsanity.com/paper/2009.06364/) (2020)
- [CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks](https://mlsanity.com/paper/2302.02213/) (2023)
- [Measuring robustness of Visual SLAM](https://mlsanity.com/paper/1910.04755/) (2019)
- [Classification robustness to common optical aberrations](https://mlsanity.com/paper/2308.15499/) (2023)
- [On the unreasonable vulnerability of transformers for image restoration -- and an easy fix](https://mlsanity.com/paper/2307.13856/) (2023)
- [Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context](https://mlsanity.com/paper/2311.17524/) (2023)
- [RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo](https://mlsanity.com/paper/2505.09368/) (2025)
- [Beware of Aliases -- Signal Preservation is Crucial for Robust Image Restoration](https://mlsanity.com/paper/2406.07435/) (2024)
- [VSLAM-LAB: A Comprehensive Framework for Visual SLAM Methods and Datasets](https://mlsanity.com/paper/2504.04457/) (2025)
- [Are Synthetic Corruptions A Reliable Proxy For Real-World Corruptions?](https://mlsanity.com/paper/2505.04835/) (2025)
- [SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification](https://mlsanity.com/paper/2505.18015/) (2025)
- [Smart Eyes for Silent Threats: VLMs and In-Context Learning for THz Imaging](https://mlsanity.com/paper/2507.15576/) (2025)
- [AIM: Amending Inherent Interpretability via Self-Supervised Masking](https://mlsanity.com/paper/2508.11502/) (2025)
- [γ-Quant: Towards Learnable Quantization for Low-bit Pattern Recognition](https://mlsanity.com/paper/2509.22448/) (2025)
- [GeoDiv: Framework For Measuring Geographical Diversity In Text-To-Image Models](https://mlsanity.com/paper/2602.22120/) (2026)
- [RAWDet-7: A Multi-Scenario Benchmark for Object Detection and Description on Quantized RAW Images](https://mlsanity.com/paper/2602.03760/) (2026)
- [SLAM Adversarial Lab: An Extensible Framework for Visual SLAM Robustness Evaluation under Adverse Conditions](https://mlsanity.com/paper/2603.17165/) (2026)

## Abstract (arXiv)

Visual SLAM is commonly evaluated on clean trajectories, although deployment failures are often caused by adverse weather, illumination, blur, and sensor artifacts. Controlled corruptions are attractive because they isolate such factors, but a synthetic stress test is useful only when it leads to the same engineering conclusion as the condition it is intended to approximate. This work examines that question for monocular SLAM. We evaluate a classical feature-based system and two learned trackers under image-space, geometry-aware, and compound corruptions, and compare their behavior with adverse conditions from 4Seasons. Rather than reducing robustness to a single trajectory error, the evaluation separates explicit tracking failure from drift accumulated by methods that remain active. The results show that learned trackers largely replace catastrophic loss with sustained, and sometimes severe, drift. More importantly, the apparent ordering of the learned systems changes with the physical fidelity of the corruption: structured rain and fog proxies preserve the real-world ordering, whereas a simple illumination proxy does not. Code is available at: https://github.com/abhaythomas/master_thesis_vslamlab_robustness.