Image-based localization using Gaussian processes

Visual localization is the process of finding the location of a camera from the appearance of the images it captures. In this work, we propose an observation model that allows the use of images for particle filter localization. To achieve this, we exploit the capabilities of Gaussian Processes to calculate the likelihood of the observation for any given pose, in contrast to methods which restrict the camera to a graph or a set of discrete poses. We evaluate this framework using different visual features as input and test its performance against laser-based localization in an indoor dataset, showing that our method requires smaller particle filter sizes while having better initialization performance.