# Ground Plane based Absolute Scale Estimation for Monocular Visual Odometry

arXiv: https://arxiv.org/abs/1903.00912 · HTML: https://mlsanity.com/paper/1903.00912/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2019.

## Built on by (most cited first, 10 of 10)

- [DF-VO: What Should Be Learnt for Visual Odometry?](https://mlsanity.com/paper/2103.00933/) (2021)
- [Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry](https://mlsanity.com/paper/2101.05995/) (2021)
- [Dense Prediction Transformer for Scale Estimation in Monocular Visual Odometry](https://mlsanity.com/paper/2210.01723/) (2022)
- [Improved Real-Time Monocular SLAM Using Semantic Segmentation on Selective Frames](https://mlsanity.com/paper/2105.00114/) (2021)
- [Learning Interpretable BEV Based VIO without Deep Neural Networks](https://mlsanity.com/paper/2109.12292/) (2021)
- [Towards Accurate Ground Plane Normal Estimation from Ego-Motion](https://mlsanity.com/paper/2212.04224/) (2022)
- [A high-precision self-supervised monocular visual odometry in foggy weather based on robust cycled generative adversarial networks and multi-task learning aided depth estimation](https://mlsanity.com/paper/2203.04812/) (2022)
- [VGGT-Align: Bridging Local Reconstruction and Global Consistency for Long-Sequence 3D Reconstruction](https://mlsanity.com/paper/2608.15260/) (2026)
- [Geometry-Constrained Monocular Scale Estimation Using Semantic Segmentation for Dynamic Scenes](https://mlsanity.com/paper/2503.04235/) (2025)
- [DP-SfM: Dual-Pixel Structure-from-Motion without Scale Ambiguity](https://mlsanity.com/paper/2605.01852/) (2026)

## Abstract (arXiv)

Recovering the absolute metric scale from a monocular camera is a challenging but highly desirable problem for monocular camera-based systems. By using different kinds of cues, various approaches have been proposed for scale estimation, such as camera height, object size etc. In this paper, firstly, we summarize different kinds of scale estimation approaches. Then, we propose a robust divide and conquer the absolute scale estimation method based on the ground plane and camera height by analyzing the advantages and disadvantages of different approaches. By using the estimated scale, an effective scale correction strategy has been proposed to reduce the scale drift during the Monocular Visual Odometry (VO) estimation process. Finally, the effectiveness and robustness of the proposed method have been verified on both public and self-collected image sequences.