Probabilistic Global Scale Estimation for MonoSLAM Based on Generic Object Detection

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...

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Main Authors: Sucar, Edgar, Hayet, Jean-Bernard
Format: Conference Proceeding
Language:English
Subjects:
Bayes methods
Cameras
Detectors
Estimation
Object detection
Simultaneous localization and mapping
Three-dimensional displays
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Hayet, Jean-Bernard
description 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.
doi_str_mv 10.1109/CVPRW.2017.135
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subjects Bayes methods
Cameras
Detectors
Estimation
Object detection
Simultaneous localization and mapping
Three-dimensional displays
title Probabilistic Global Scale Estimation for MonoSLAM Based on Generic Object Detection
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