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

## TL;DR

This paper introduces a Bayesian method for estimating the global scale in monocular SLAM by leveraging generic object detection and height priors, eliminating the need for data association across frames.

## Contribution

It presents a novel probabilistic framework that integrates object height priors into monocular SLAM without requiring data association over time.

## Key findings

- Effective scale estimation demonstrated on multiple object classes.
- No data association needed across video frames.
- Promising experimental results validate the approach.

## Abstract

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.