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

Probabilistic Global Scale Estimation for MonoSLAM Based on Generic Object Detection