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

Source: [Probabilistic Global Scale Estimation for MonoSLAM Based on Generic Object Detection](<https://www.sota2.com/research/paper/68fa63691e209131ce9fe895>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:33.740Z

Authors: Edgar Sucar, Jean-Bernard Hayet

Published: 2017-05-27T20:14:31.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1705.09860>)

[PDF](<https://arxiv.org/pdf/1705.09860.pdf>)