Robust Direct Visual Localisation using Normalised Information Distance

We present an information-theoretic approach for direct localisation of a monocular camera within a 3D appearance prior. In contrast to existing direct visual localisation methods based on minimising photometric error, an information-theoretic metric allows us to compare the whole image without relying on individual pixel values, yielding robustness to changes in the appearance of the scene due to lighting, camera motion, occlusions and sensor modality. Using a low-fidelity textured 3D model of the environment, we synthesise virtual images at a candidate pose within the model. We use the Normalised Information Distance (NID) metric to evaluate the appearance match between the camera image and the virtual image, and present a derivation of analytical NID derivatives for the SE(3) direct localisation problem, along with an efficient GPGPU implementation capable of online processing. We present results showing successful online visual localisation under significant appearance change both in a synthetic indoor environment and outdoors with real-world data from a vehicle-mounted camera.