# Bayesian Scale Estimation for Monocular SLAM Based on Generic Object Detection for Correcting Scale Drift

arXiv: https://arxiv.org/abs/1711.02768 · HTML: https://mlsanity.com/paper/1711.02768/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [Semantic Visual Simultaneous Localization and Mapping: A Survey](https://mlsanity.com/paper/2209.06428/) (2022)
- [Camera Height Doesn't Change: Unsupervised Training for Metric Monocular Road-Scene Depth Estimation](https://mlsanity.com/paper/2312.04530/) (2023)
- [Anchor Distance for 3D Multi-Object Distance Estimation from 2D Single Shot](https://mlsanity.com/paper/2101.10399/) (2021)
- [DP-SfM: Dual-Pixel Structure-from-Motion without Scale Ambiguity](https://mlsanity.com/paper/2605.01852/) (2026)

## Abstract (arXiv)

This work proposes a new, online algorithm for estimating the local scale correction to apply to the output of a monocular SLAM system and obtain an as faithful as possible metric reconstruction of the 3D map and of the camera trajectory. Within a Bayesian framework, it integrates observations from a deep-learning based generic object detector and a prior on the evolution of the scale drift. For each observation class, a predefined prior on the heights of the class objects is used. This allows to define the observations likelihood. Due to the scale drift inherent to monocular SLAM systems, we integrate a rough model on the dynamics of scale drift. Quantitative evaluations of the system are presented on the KITTI dataset, and compared with different approaches. The results show a superior performance of our proposal in terms of relative translational error when compared to other monocular systems.