Probabilistic Global Scale Estimation for MonoSLAM Based on Generic Object Detection
This addresses scale ambiguity in monocular SLAM for robotics or AR applications, but it is incremental as it builds on existing detection and SLAM methods.
The paper tackles the problem of estimating global scale in monocular SLAM by integrating height priors from generic object detection into a Bayesian framework, achieving promising results in experiments with various object classes.
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.