# UNRealNet: Learning Uncertainty-Aware Navigation Features from High-Fidelity Scans of Real Environments

arXiv: https://arxiv.org/abs/2407.08720 · HTML: https://mlsanity.com/paper/2407.08720/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2024.

## Built on by (most cited first, 4 of 4)

- [MapEx: Indoor Structure Exploration with Probabilistic Information Gain from Global Map Predictions](https://mlsanity.com/paper/2409.15590/) (2024)
- [PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain](https://mlsanity.com/paper/2409.03005/) (2024)
- [ZeST: A VLM-based Zero-Shot Traversability Navigation for Unknown Environments](https://mlsanity.com/paper/2508.19131/) (2025)
- [ProTerrain: Probabilistic Physics-Informed Rough Terrain World Modeling](https://mlsanity.com/paper/2510.19364/) (2025)

## Abstract (arXiv)

Traversability estimation in rugged, unstructured environments remains a challenging problem in field robotics. Often, the need for precise, accurate traversability estimation is in direct opposition to the limited sensing and compute capability present on affordable, small-scale mobile robots. To address this issue, we present a novel method to learn [u]ncertainty-aware [n]avigation features from high-fidelity scans of [real]-world environments (UNRealNet). This network can be deployed on-robot to predict these high-fidelity features using input from lower-quality sensors. UNRealNet predicts dense, metric-space features directly from single-frame lidar scans, thus reducing the effects of occlusion and odometry error. Our approach is label-free, and is able to produce traversability estimates that are robot-agnostic. Additionally, we can leverage UNRealNet's predictive uncertainty to both produce risk-aware traversability estimates, and refine our feature predictions over time. We find that our method outperforms traditional local mapping and inpainting baselines by up to 40%, and demonstrate its efficacy on multiple legged platforms.