# Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry

arXiv: https://arxiv.org/abs/2101.05995 · HTML: https://mlsanity.com/paper/2101.05995/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 15 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving Applications](https://mlsanity.com/paper/2004.05560/) (2020)
- [EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association](https://mlsanity.com/paper/2004.12730/) (2020)
- [Self-Supervised Scale Recovery for Monocular Depth and Egomotion Estimation](https://mlsanity.com/paper/2009.03787/) (2020)
- [Ground Plane based Absolute Scale Estimation for Monocular Visual Odometry](https://mlsanity.com/paper/1903.00912/) (2019)
- [Enhancing self-supervised monocular depth estimation with traditional visual odometry](https://mlsanity.com/paper/1908.03127/) (2019)

## Built on by (most cited first, 6 of 6)

- [Dense Prediction Transformer for Scale Estimation in Monocular Visual Odometry](https://mlsanity.com/paper/2210.01723/) (2022)
- [MonoPP: Metric-Scaled Self-Supervised Monocular Depth Estimation by Planar-Parallax Geometry in Automotive Applications](https://mlsanity.com/paper/2411.19717/) (2024)
- [Accurate and Robust Object-oriented SLAM with 3D Quadric Landmark Construction in Outdoor Environment](https://mlsanity.com/paper/2110.08977/) (2021)
- [Geometry-Constrained Monocular Scale Estimation Using Semantic Segmentation for Dynamic Scenes](https://mlsanity.com/paper/2503.04235/) (2025)
- [Learn Your Scales: Towards Scale-Consistent Generative Novel View Synthesis](https://mlsanity.com/paper/2503.15412/) (2025)
- [UniMetric: Universal Metric Scale Recovery for Visual Geometry Foundation Models](https://mlsanity.com/paper/2610.16945/) (2026)

## Abstract (arXiv)

Scale ambiguity is a fundamental problem in monocular visual odometry. Typical solutions include loop closure detection and environment information mining. For applications like self-driving cars, loop closure is not always available, hence mining prior knowledge from the environment becomes a more promising approach. In this paper, with the assumption of a constant height of the camera above the ground, we develop a light-weight scale recovery framework leveraging an accurate and robust estimation of the ground plane. The framework includes a ground point extraction algorithm for selecting high-quality points on the ground plane, and a ground point aggregation algorithm for joining the extracted ground points in a local sliding window. Based on the aggregated data, the scale is finally recovered by solving a least-squares problem using a RANSAC-based optimizer. Sufficient data and robust optimizer enable a highly accurate scale recovery. Experiments on the KITTI dataset show that the proposed framework can achieve state-of-the-art accuracy in terms of translation errors, while maintaining competitive performance on the rotation error. Due to the light-weight design, our framework also demonstrates a high frequency of 20Hz on the dataset.