# BEV-ODOM: Reducing Scale Drift in Monocular Visual Odometry with BEV Representation

arXiv: https://arxiv.org/abs/2411.10195 · HTML: https://mlsanity.com/paper/2411.10195/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2024.

## Built on by (most cited first, 3 of 3)

- [LoGoPlanner: Localization Grounded Navigation Policy with Metric-aware Visual Geometry](https://mlsanity.com/paper/2512.19629/) (2025)
- [Astra: Toward General-Purpose Mobile Robots via Hierarchical Multimodal Learning](https://mlsanity.com/paper/2506.06205/) (2025)
- [MVOFormer: Flow-Semantic Transformer for Robust Monocular Visual Odometry](https://mlsanity.com/paper/2606.16474/) (2026)

## Abstract (arXiv)

Monocular visual odometry (MVO) is vital in autonomous navigation and robotics, providing a cost-effective and flexible motion tracking solution, but the inherent scale ambiguity in monocular setups often leads to cumulative errors over time. In this paper, we present BEV-ODOM, a novel MVO framework leveraging the Bird's Eye View (BEV) Representation to address scale drift. Unlike existing approaches, BEV-ODOM integrates a depth-based perspective-view (PV) to BEV encoder, a correlation feature extraction neck, and a CNN-MLP-based decoder, enabling it to estimate motion across three degrees of freedom without the need for depth supervision or complex optimization techniques. Our framework reduces scale drift in long-term sequences and achieves accurate motion estimation across various datasets, including NCLT, Oxford, and KITTI. The results indicate that BEV-ODOM outperforms current MVO methods, demonstrating reduced scale drift and higher accuracy.