Context-coherent scenes of objects for camera pose estimation

We propose an approach to vision-based pose estimation using object recognition and identity. Whereas feature based scene recognition and pose estimation methods are well established as effective means for estimating motion and recognizing locations, feature-based methods depend critically on the detection of common local features from one view of a scene to another. We focus on place recognition and pose change estimation in the context of large changes in viewing position, even to the extent that no common surfaces are seen between the two views. Our approach is based on using object identities and their inter-relationship to compute pose change. An important secondary outcome of our method is that it simultaneously infers the 3D poses of objects in the scene that are used as features. Such an object-based approach is inspired by a vast literature on human perception and has the potential for great robustness, albeit at the expense of accuracy. We propose a formulation of the problem using pairwise contextual constraints and develop an efficient algorithmic solution. We validate the approach and quantify its performance using the publicly available TUM SLAM dataset [1].