Direct 3D model-based tracking in omnidirectional images robust to large inter-frame motion

Abstract

This paper tackles direct 3D model-based pose tracking. It considers the Photometric Gaussian Mixtures (PGM) transform of omnidirectional images as direct features. The contributions include an adaptation of the pose optimization to omnidirectional cameras and a rethink of the initialization and optimization rules of the PGM extent. These enhancements produce a giant leap in the convergence domain width. Application to images acquired onboard a mobile robot within an urban environment described by a large 3D colored point cloud shows significant robustness to large interframe motion, compared to approaches that directly use pixel brightness as direct features.