# Learning Off-Road Terrain Traversability with Self-Supervisions Only

arXiv: https://arxiv.org/abs/2305.18896 · HTML: https://mlsanity.com/paper/2305.18896/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 35 references that are in the map.
- First posted on arXiv 30 May 2023.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Self-labelling via simultaneous clustering and representation learning](https://mlsanity.com/paper/1911.05371/) (2019)
- [CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows](https://mlsanity.com/paper/2107.12571/) (2021)
- [FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows](https://mlsanity.com/paper/2111.07677/) (2021)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [RELLIS-3D Dataset: Data, Benchmarks and Analysis](https://mlsanity.com/paper/2011.12954/) (2020)
- [GANav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments](https://mlsanity.com/paper/2103.04233/) (2021)
- [Self-Supervised Visual Terrain Classification from Unsupervised Acoustic Feature Learning](https://mlsanity.com/paper/1912.03227/) (2019)
- [Safe Robot Navigation via Multi-Modal Anomaly Detection](https://mlsanity.com/paper/2001.07934/) (2020)
- [GONet: A Semi-Supervised Deep Learning Approach For Traversability Estimation](https://mlsanity.com/paper/1803.03254/) (2018)
- [Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy](https://mlsanity.com/paper/1610.01238/) (2016)
- [How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability](https://mlsanity.com/paper/2209.10788/) (2022)
- [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)
- [Multi-Modal Anomaly Detection for Unstructured and Uncertain Environments](https://mlsanity.com/paper/2012.08637/) (2020)
- [SLiDE: Self-supervised LiDAR De-snowing through Reconstruction Difficulty](https://mlsanity.com/paper/2208.04043/) (2022)

## Built on by (most cited first, 27 of 27)

- [RoadRunner -- Learning Traversability Estimation for Autonomous Off-road Driving](https://mlsanity.com/paper/2402.19341/) (2024)
- [EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy](https://mlsanity.com/paper/2311.06234/) (2023)
- [Identifying Terrain Physical Parameters from Vision -- Towards Physical-Parameter-Aware Locomotion and Navigation](https://mlsanity.com/paper/2408.16567/) (2024)
- [PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain](https://mlsanity.com/paper/2409.03005/) (2024)
- [Advancing Off-Road Autonomous Driving: The Large-Scale ORAD-3D Dataset and Comprehensive Benchmarks](https://mlsanity.com/paper/2510.16500/) (2025)
- [Reinforcement Learning for Wheeled Mobility on Vertically Challenging Terrain](https://mlsanity.com/paper/2409.02383/) (2024)
- [Autonomous Driving in Unstructured Environments: How Far Have We Come?](https://mlsanity.com/paper/2410.07701/) (2024)
- [GeNIE: A Generalizable Navigation System for In-the-Wild Environments](https://mlsanity.com/paper/2506.17960/) (2025)
- [Evidential Semantic Mapping in Off-road Environments with Uncertainty-aware Bayesian Kernel Inference](https://mlsanity.com/paper/2403.14138/) (2024)
- [Safe Navigation in Unstructured Environments by Minimizing Uncertainty in Control and Perception](https://mlsanity.com/paper/2306.14601/) (2023)
- [UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimation](https://mlsanity.com/paper/2403.02642/) (2024)
- [Learning Semantic Traversability with Egocentric Video and Automated Annotation Strategy](https://mlsanity.com/paper/2406.02989/) (2024)
- [ROD: RGB-Only Fast and Efficient Off-road Freespace Detection](https://mlsanity.com/paper/2508.08697/) (2025)
- [VertiSelector: Automatic Curriculum Learning for Wheeled Mobility on Vertically Challenging Terrain](https://mlsanity.com/paper/2409.17469/) (2024)
- [Uncertainty-aware Semantic Mapping in Off-road Environments with Dempster-Shafer Theory of Evidence](https://mlsanity.com/paper/2405.06265/) (2024)
- [Trajectory-based Road Autolabeling with Lidar-Camera Fusion in Winter Conditions](https://mlsanity.com/paper/2412.02370/) (2024)
- [TE-NeXt: A LiDAR-Based 3D Sparse Convolutional Network for Traversability Estimation](https://mlsanity.com/paper/2406.01395/) (2024)
- [Self-supervised cost of transport estimation for multimodal path planning](https://mlsanity.com/paper/2412.06101/) (2024)
- [METAVerse: Meta-Learning Traversability Cost Map for Off-Road Navigation](https://mlsanity.com/paper/2307.13991/) (2023)
- [DA-RAW: Domain Adaptive Object Detection for Real-World Adverse Weather Conditions](https://mlsanity.com/paper/2309.08152/) (2023)
- [TADAP: Trajectory-Aided Drivable area Auto-labeling with Pre-trained self-supervised features in winter driving conditions](https://mlsanity.com/paper/2312.12954/) (2023)
- [Label Correction for Road Segmentation Using Road-side Cameras](https://mlsanity.com/paper/2502.01281/) (2025)
- [Scene-Agnostic Traversability Labeling and Estimation via a Multimodal Self-supervised Framework](https://mlsanity.com/paper/2508.18249/) (2025)
- [Mars Traversability Prediction: A Multi-modal Self-supervised Approach for Costmap Generation](https://mlsanity.com/paper/2509.11082/) (2025)
- [SyNeT: Synthetic Negatives for Traversability Learning](https://mlsanity.com/paper/2602.00814/) (2026)
- [Towards All-Day Perception for Off-Road Driving: A Large-Scale Multispectral Dataset and Comprehensive Benchmark](https://mlsanity.com/paper/2604.27499/) (2026)
- [Tracking the Invisible: Self-Supervised Wheel Track Detection with Thermal-RGB Fusion](https://mlsanity.com/paper/2610.13872/) (2026)

## Abstract (arXiv)

Estimating the traversability of terrain should be reliable and accurate in diverse conditions for autonomous driving in off-road environments. However, learning-based approaches often yield unreliable results when confronted with unfamiliar contexts, and it is challenging to obtain manual annotations frequently for new circumstances. In this paper, we introduce a method for learning traversability from images that utilizes only self-supervision and no manual labels, enabling it to easily learn traversability in new circumstances. To this end, we first generate self-supervised traversability labels from past driving trajectories by labeling regions traversed by the vehicle as highly traversable. Using the self-supervised labels, we then train a neural network that identifies terrains that are safe to traverse from an image using a one-class classification algorithm. Additionally, we supplement the limitations of self-supervised labels by incorporating methods of self-supervised learning of visual representations. To conduct a comprehensive evaluation, we collect data in a variety of driving environments and perceptual conditions and show that our method produces reliable estimations in various environments. In addition, the experimental results validate that our method outperforms other self-supervised traversability estimation methods and achieves comparable performances with supervised learning methods trained on manually labeled data.