Monte Carlo Localization and registration to prior data for outdoor navigation

GPS has become the de facto standard for obtaining a global position estimate during outdoor autonomous navigation. However, GPS can become degraded due to occlusion or interference, to the detriment of autonomous performance. In addition, GPS positions must be aligned with prior data, a tedious and continual process. This work presents a solution to these two problems based on learning generic observation models in the presence of GPS to use in its absence. The models are non-parametric and compared to traditional approaches require few assumptions about either the prior data available or a robot's onboard sensors. Along with allowing for localization to prior data under GPS-denied conditions, this learning approach can be coupled with an EM procedure to automatically register GPS and prior data positions. Experimental results are presented based on data from more than 15 km of autonomous navigation through challenging outdoor terrain.