# Probabilistic Global Scale Estimation for MonoSLAM Based on Generic Object Detection

arXiv: https://arxiv.org/abs/1705.09860 · HTML: https://mlsanity.com/paper/1705.09860/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2017.

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

- [Visual Attention-based Self-supervised Absolute Depth Estimation using Geometric Priors in Autonomous Driving](https://mlsanity.com/paper/2205.08780/) (2022)
- [Road-aware Monocular Structure from Motion and Homography Estimation](https://mlsanity.com/paper/2112.08635/) (2021)
- [UniMetric: Universal Metric Scale Recovery for Visual Geometry Foundation Models](https://mlsanity.com/paper/2610.16945/) (2026)

## Abstract (arXiv)

This paper proposes a novel method to estimate the global scale of a 3D reconstructed model within a Kalman filtering-based monocular SLAM algorithm. Our Bayesian framework integrates height priors over the detected objects belonging to a set of broad predefined classes, based on recent advances in fast generic object detection. Each observation is produced on single frames, so that we do not need a data association process along video frames. This is because we associate the height priors with the image region sizes at image places where map features projections fall within the object detection regions. We present very promising results of this approach obtained on several experiments with different object classes.