# BEVRender: Vision-based Cross-view Vehicle Registration in Off-road GNSS-denied Environment

arXiv: https://arxiv.org/abs/2405.09001 · HTML: https://mlsanity.com/paper/2405.09001/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2024.

## Built on by (most cited first, 5 of 5)

- [Map It Anywhere (MIA): Empowering Bird's Eye View Mapping using Large-scale Public Data](https://mlsanity.com/paper/2407.08726/) (2024)
- [Spatiotemporal Contrastive Learning for Cross-View Video Localization in Unstructured Off-road Terrains](https://mlsanity.com/paper/2506.05250/) (2025)
- [Evaluating Global Geo-alignment for Precision Learned Autonomous Vehicle Localization using Aerial Data](https://mlsanity.com/paper/2503.13896/) (2025)
- [BEV-Patch-PF: Particle Filtering with BEV-Aerial Feature Matching for Off-Road Geo-Localization](https://mlsanity.com/paper/2512.15111/) (2025)
- [TACO: Trajectory Aligning Cross-view Optimisation](https://mlsanity.com/paper/2605.03315/) (2026)

## Abstract (arXiv)

We introduce BEVRender, a novel learning based approach for the localization of ground vehicles in Global Navigation Satellite System(GNSS)-denied off-road scenarios. These environments are typically challenging for conventional vision-based state estimation due to the lack of distinct visual landmarks and the instability of vehicle poses. To address this, BEVRender generates high-quality local bird's-eye-view(BEV) images of the local terrain. Subsequently, these images are aligned with a geo referenced aerial map through template matching to achieve accurate cross-view registration. Our approach overcomes the inherent limitations of visual inertial odometry systems and the substantial storage requirements of image-retrieval localization strategies, which are susceptible to drift and scalability issues, respectively. Extensive experimentation validates BEVRender's advancement over existing GNSS-denied visual localization methods, demonstrating notable enhancements in both localization accuracy and update frequency.