This paper is concerned with large-scale localisation at city scales with monocular cameras. Our primary motivation lies with the development of autonomous road vehicles - an application domain in which low-cost sensing is particularly important. Here we present a method for localising against a textured 3-dimensional prior mesh using a monocular camera. We first present a system for generating and texturing the prior using a LIDAR scanner and camera. We then describe how we can localise against that prior with a single camera, using an information-theoretic measure of image similarity. This process requires dealing with the distortions induced by a wide-angle camera. We present and justify an interesting approach to this issue in which we distort the prior map into the image rather than vice-versa. Finally we explain how the general purpose computation functionality of a modern GPU is particularly apt for our task, allowing us to run the system in real time. We present results showing centimetre-level localisation accuracy through a city over six kilometres.

This paper is about extending the reach and endurance of outdoor localisation using stereo vision. At the heart of the localisation is the fundamental task of discovering feature correspondences between recorded and live images. One aspect of this problem involves deciding where to look for correspondences in an image and the second is deciding what to look for. This latter point, which is the main focus of our paper, requires understanding how and why the appearance of visual features can change over time. In particular, such knowledge allows us to better deal with abrupt and challenging changes in lighting. We show how by instantiating a parallel image processing stream which operates on illumination-invariant images, we can substantially improve the performance of an outdoor visual navigation system. We will demonstrate, explain and analyse the effect of the RGB to illumination-invariant transformation and suggest that for little cost it becomes a viable tool for those concerned with having robots operate for long periods outdoors.

Robust and reliable visual localisation at any time of day is an essential component towards low-cost autonomy for road vehicles. We present a method to perform online 6-DoF visual localisation across a wide range of outdoor illumination conditions throughout the day and night using a 3D scene prior collected by a survey vehicle. We propose the use of a one-dimensional illumination invariant colour space which stems from modelling the spectral properties of the camera and scene illumination in conjunction. We combine our previous work on Localisation with Appearance of Prior Structure (LAPS) with this illumination invariant colour space to demonstrate a marked improvement in our ability to localise throughout the day compared to using a conventional RGB colour space. Our ultimate goal is robust and reliable any-time localisation — an attractive proposition for low-cost autonomy for road vehicles. Accordingly, we demonstrate our technique using 32km of data collected over a full 24-hour period from a road vehicle.

This paper is about pose estimation using monocular cameras with a 3D laser pointcloud as a workspace prior. We have in mind autonomous transport systems in which low cost vehicles equipped with monocular cameras are furnished with preprocessed 3D lidar workspaces surveys. Our inherently cross-modal approach offers robustness to changes in scene lighting and is computationally cheap. At the heart of our approach lies inference of camera motion by minimisation of the Normalised Information Distance (NID) between the appearance of 3D lidar data reprojected into overlapping images. Results are presented which demonstrate the applicability of this approach to the localisation of a camera against a lidar pointcloud using data gathered from a road vehicle.