# GONet: A Semi-Supervised Deep Learning Approach For Traversability Estimation

arXiv: https://arxiv.org/abs/1803.03254 · HTML: https://mlsanity.com/paper/1803.03254/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2018.

## Built on by (most cited first, 29 of 29)

- [BridgeData V2: A Dataset for Robot Learning at Scale](https://mlsanity.com/paper/2308.12952/) (2023)
- [Navigation World Models](https://mlsanity.com/paper/2412.03572/) (2024)
- [Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation](https://mlsanity.com/paper/2112.05251/) (2021)
- [QUAR-VLA: Vision-Language-Action Model for Quadruped Robots](https://mlsanity.com/paper/2312.14457/) (2023)
- [Surround-view Fisheye Camera Perception for Automated Driving: Overview, Survey and Challenges](https://mlsanity.com/paper/2205.13281/) (2022)
- [Autonomous Improvement of Instruction Following Skills via Foundation Models](https://mlsanity.com/paper/2407.20635/) (2024)
- [Learning Off-Road Terrain Traversability with Self-Supervisions Only](https://mlsanity.com/paper/2305.18896/) (2023)
- [Visual Representation Learning for Preference-Aware Path Planning](https://mlsanity.com/paper/2109.08968/) (2021)
- [Asking for Help: Failure Prediction in Behavioral Cloning through Value Approximation](https://mlsanity.com/paper/2302.04334/) (2023)
- [Towards Unified World Models for Visual Navigation via Memory-Augmented Planning and Foresight](https://mlsanity.com/paper/2510.08713/) (2025)
- [Learning to Drive Anywhere with Model-Based Reannotation](https://mlsanity.com/paper/2505.05592/) (2025)
- [FlowNav: Combining Flow Matching and Depth Priors for Efficient Navigation](https://mlsanity.com/paper/2411.09524/) (2024)
- [Sparse Video Generation Propels Real-World Beyond-the-View Vision-Language Navigation](https://mlsanity.com/paper/2602.05827/) (2026)
- [Language-Conditioned World Modeling for Visual Navigation](https://mlsanity.com/paper/2603.26741/) (2026)
- [System Configuration and Navigation of a Guide Dog Robot: Toward Animal Guide Dog-Level Guiding Work](https://mlsanity.com/paper/2210.13368/) (2022)
- [CoverNav: Cover Following Navigation Planning in Unstructured Outdoor Environment with Deep Reinforcement Learning](https://mlsanity.com/paper/2308.06594/) (2023)
- [UrbanNav: Learning Language-Guided Urban Navigation from Web-Scale Human Trajectories](https://mlsanity.com/paper/2512.09607/) (2025)
- [NavWM: A Unified Navigation World Model for Foresight-Driven Planning](https://mlsanity.com/paper/2606.24101/) (2026)
- [Depth360: Self-supervised Learning for Monocular Depth Estimation using Learnable Camera Distortion Model](https://mlsanity.com/paper/2110.10415/) (2021)
- [GuideNav: User-Informed Development of a Vision-Only Robotic Navigation Assistant For Blind Travelers](https://mlsanity.com/paper/2512.06147/) (2025)
- [MetricNet: Recovering Metric Scale in Generative Navigation Policies](https://mlsanity.com/paper/2509.13965/) (2025)
- [Action-conditioned Deep Visual Prediction with RoAM, a new Indoor Human Motion Dataset for Autonomous Robots](https://mlsanity.com/paper/2306.15852/) (2023)
- [Uncertainty-Aware World Model for Aerial Image-Goal Navigation](https://mlsanity.com/paper/2608.05597/) (2026)
- [Analysis of LiDAR Configurations on Off-road Semantic Segmentation Performance](https://mlsanity.com/paper/2306.16551/) (2023)
- [Depth-guided Free-space Segmentation for a Mobile Robot](https://mlsanity.com/paper/2311.01966/) (2023)
- [Enhancing Robustness of Indoor Robotic Navigation with Free-Space Segmentation Models Against Adversarial Attacks](https://mlsanity.com/paper/2402.08763/) (2024)
- [ACME: A Multi-Cultural, Multi-Embodiment Social-Navigation Dataset](https://mlsanity.com/paper/2607.21964/) (2026)
- [TravKAN: Fast and Interpretable Nonlinear Traversability Analysis with Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2608.02320/) (2026)
- [Tracking the Invisible: Self-Supervised Wheel Track Detection with Thermal-RGB Fusion](https://mlsanity.com/paper/2610.13872/) (2026)

## Abstract (arXiv)

We present semi-supervised deep learning approaches for traversability estimation from fisheye images. Our method, GONet, and the proposed extensions leverage Generative Adversarial Networks (GANs) to effectively predict whether the area seen in the input image(s) is safe for a robot to traverse. These methods are trained with many positive images of traversable places, but just a small set of negative images depicting blocked and unsafe areas. This makes the proposed methods practical. Positive examples can be collected easily by simply operating a robot through traversable spaces, while obtaining negative examples is time consuming, costly, and potentially dangerous. Through extensive experiments and several demonstrations, we show that the proposed traversability estimation approaches are robust and can generalize to unseen scenarios. Further, we demonstrate that our methods are memory efficient and fast, allowing for real-time operation on a mobile robot with single or stereo fisheye cameras. As part of our contributions, we open-source two new datasets for traversability estimation. These datasets are composed of approximately 24h of videos from more than 25 indoor environments. Our methods outperform baseline approaches for traversability estimation on these new datasets.