# Safe Robot Navigation via Multi-Modal Anomaly Detection

arXiv: https://arxiv.org/abs/2001.07934 · HTML: https://mlsanity.com/paper/2001.07934/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2020.

## Built on by (most cited first, 30 of 34)

- [A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect](https://mlsanity.com/paper/2401.16402/) (2024)
- [Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots](https://mlsanity.com/paper/2405.01792/) (2024)
- [Fast Traversability Estimation for Wild Visual Navigation](https://mlsanity.com/paper/2305.08510/) (2023)
- [Uncertainty-aware Latent Safety Filters for Avoiding Out-of-Distribution Failures](https://mlsanity.com/paper/2505.00779/) (2025)
- [WayFAST: Navigation with Predictive Traversability in the Field](https://mlsanity.com/paper/2203.12071/) (2022)
- [Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees](https://mlsanity.com/paper/2201.08355/) (2022)
- [Learning Off-Road Terrain Traversability with Self-Supervisions Only](https://mlsanity.com/paper/2305.18896/) (2023)
- [RoadRunner -- Learning Traversability Estimation for Autonomous Off-road Driving](https://mlsanity.com/paper/2402.19341/) (2024)
- [Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain](https://mlsanity.com/paper/2208.01329/) (2022)
- [ScaTE: A Scalable Framework for Self-Supervised Traversability Estimation in Unstructured Environments](https://mlsanity.com/paper/2209.06522/) (2022)
- [Play to the Score: Stage-Guided Dynamic Multi-Sensory Fusion for Robotic Manipulation](https://mlsanity.com/paper/2408.01366/) (2024)
- [Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision](https://mlsanity.com/paper/2404.07110/) (2024)
- [Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion](https://mlsanity.com/paper/2204.01146/) (2022)
- [Learning Robotic Navigation from Experience: Principles, Methods, and Recent Results](https://mlsanity.com/paper/2212.06759/) (2022)
- [STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience](https://mlsanity.com/paper/2309.15302/) (2023)
- [Identifying Terrain Physical Parameters from Vision -- Towards Physical-Parameter-Aware Locomotion and Navigation](https://mlsanity.com/paper/2408.16567/) (2024)
- [Interactive Navigation in Environments with Traversable Obstacles Using Large Language and Vision-Language Models](https://mlsanity.com/paper/2310.08873/) (2023)
- [Using Visual Anomaly Detection for Task Execution Monitoring](https://mlsanity.com/paper/2107.14206/) (2021)
- [Visual-Tactile Cross-Modal Data Generation using Residue-Fusion GAN with Feature-Matching and Perceptual Losses](https://mlsanity.com/paper/2107.05468/) (2021)
- [Safe Navigation in Unstructured Environments by Minimizing Uncertainty in Control and Perception](https://mlsanity.com/paper/2306.14601/) (2023)
- [ROD: RGB-Only Fast and Efficient Off-road Freespace Detection](https://mlsanity.com/paper/2508.08697/) (2025)
- [Your Vision-Language-Action Model Already Has Attention Heads For Path Deviation Detection](https://mlsanity.com/paper/2603.13782/) (2026)
- [An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots](https://mlsanity.com/paper/2209.09786/) (2022)
- [Sensing Anomalies as Potential Hazards: Datasets and Benchmarks](https://mlsanity.com/paper/2110.14706/) (2021)
- [Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications](https://mlsanity.com/paper/2209.06855/) (2022)
- [Out-of-Distribution Detection for Adaptive Computer Vision](https://mlsanity.com/paper/2305.09293/) (2023)
- [An Expert Ensemble for Detecting Anomalous Scenes, Interactions, and Behaviors in Autonomous Driving](https://mlsanity.com/paper/2502.16389/) (2025)
- [METAVerse: Meta-Learning Traversability Cost Map for Off-Road Navigation](https://mlsanity.com/paper/2307.13991/) (2023)
- [Random Similarity Isolation Forests](https://mlsanity.com/paper/2502.19122/) (2025)
- [Towards All-Day Perception for Off-Road Driving: A Large-Scale Multispectral Dataset and Comprehensive Benchmark](https://mlsanity.com/paper/2604.27499/) (2026)

## Abstract (arXiv)

Navigation in natural outdoor environments requires a robust and reliable traversability classification method to handle the plethora of situations a robot can encounter. Binary classification algorithms perform well in their native domain but tend to provide overconfident predictions when presented with out-of-distribution samples, which can lead to catastrophic failure when navigating unknown environments. We propose to overcome this issue by using anomaly detection on multi-modal images for traversability classification, which is easily scalable by training in a self-supervised fashion from robot experience. In this work, we evaluate multiple anomaly detection methods with a combination of uni- and multi-modal images in their performance on data from different environmental conditions. Our results show that an approach using a feature extractor and normalizing flow with an input of RGB, depth and surface normals performs best. It achieves over 95% area under the ROC curve and is robust to out-of-distribution samples.