Robust direct visual odometry using mutual information

Abstract

Robust vision-based state estimation in real-world indoor and outdoor environments is a challenging problem due to the combination of drastic lighting changes and limited dynamic range of commodity cameras. This limitation is at odds with the fundamental constancy of illumination assumption made in image-intensity based tracking methodologies. We present and experimentally validate a Mutual Information (MI) based dense rigid body tracking algorithm that is demonstrably robust to drastic illumination changes, and compare the performance of this algorithm to a canonical Sum of Squared Differences based Lucas-Kanade tracking formulation. Further, we propose a novel approach that combines the robustness benefits of information-based measures and the speed of traditional intensity based Lucas-Kanade tracking for robust state estimation in real-time.