Visual localization in fused image and laser range data

This paper reports on a method for tracking a camera system within an a priori known map constructed from co-registered 3D light detection and ranging (LIDAR) and omnidirectional image data. Our method pre-processes the raw 3D LIDAR and camera data to produce a sparse map that can scale to city-size environments. From the original LIDAR and camera data we extract visual features and identify those that are most robust to varying viewpoint. This allows us to include only the visual features that are most useful for localization in the map. Additionally, we quantize the visual features using a vocabulary tree to further reduce the map's file size. We then use vision-based localization to track the vehicle's motion through the map. We present results on urban data collected with Ford Motor Company's autonomous vehicle testbed. In our experiments the map is built using urban data from winter 2009, and localization is performed using data collected in fall 2010 and winter 2011. This demonstrates our algorithm's robustness to temporal changes in the environment.