Accurate Plane Estimation Within a Holistic Probabilistic Framework

Accurate 3D plane estimation in complex environments is an important functionality in many robotics applications such as navigation, manipulation, human-machine interaction. Following recent research in coherent geometrical contextual reasoning and object recognition, this paper proposes a joint probabilistic model which uses the results of wireframe feature detection to facilitate refinement of supporting plane estimation. By maximizing the probability of the joint model, our method has the capability of simultaneously estimating multiple 3D surfaces. The experiments using both synthetic data and an indoor mobile robot scenario demonstrate the benefits of our coherent model approach.