Monte Carlo Localization on Gaussian Process Occupancy Maps for Urban Environments

Map-aided localization methods have been employed for vehicle localization to overcome the limitations of global navigation satellite system (GNSS) devices. In this solution, sensor information is matched to the environment map to determine the vehicle position. Occupancy grid maps (OGMs) have been adapted for map-aided localization. However, there are known drawbacks of OGM, such as the environment discretization, the assumption of independence between grid cells, and the need for dense measurements. In recent years, Gaussian process occupancy map (GPOM) was developed to suppress some of the OGM limitations. GPOM enables the computation of the likelihood of occupancy at any location, even if not directly observed by the sensor, thus representing the environment in a continuous manner. Taking into account the superiority of GPOM over OGM, we devise a novel vehicle localization technique for urban environments. This solution enables more accurate localization due to the use of a representation that better models the real environment. The development of the proposed method is based on Monte Carlo localization, which is a popular map-aided localization method. Two road features commonly found in urban cities were chosen to build the maps: road curbs and road markings. Specifically, the proposed localization method relies on a GPOM constructed with curb data and an OGM built with road marking data. Experiments were performed in real urban environments. Maps were intentionally generated using sparse light detection and ranging (LIDAR) data to verify the localization in non-observed areas. The localization system was evaluated by comparing the results with a high precision GNSS device.