Map-based localization for intelligent vehicles from bi-sensor data fusion

Abstract

Accurate localization is a challenging task for intelligent vehicles (IVs). Map-based localization methods are promising as they have no cumulative errors and high accuracy. Most methods are based on a single sensor, such as a camera or laser rangefinder (LRF). However, these sensors have disadvantages. Thus, this study proposes a map-based localization method from bi-sensor data fusion. We proposed a method using a camera, LRF, differential Global Positioning System, Inertial Navigation System to generate a multi-scale map. The multi-map consists of scenario nodes, and each node encodes scenario features, road 3D structure, and trajectories. Based on the multi-scale map, we propose an accurate localization method that includes coarse, node-level, and metric localizations. A bitopological localization model is set up for coarse localization. In this model, we use previous feature matching results to predict the current position. The features include image and light detection and ranging (LIDAR) data, and each of them set up one topological model. The two models make up a bitopological localization model. In node-level localization, we extract the LIDAR and image features and match them with the multi-scale map. The closest node is found by K-nearest neighbors from multiple feature spaces. In this step, we keep the image feature extraction in mind and propose a novel LIDAR feature extraction method called the LIDAR-image feature. In metric localization, we solve the perspective n-points problem between the current position and the closest node to compute the pose of IVs. In the experiments, our method has been tested on open road and campus. Each route covers different times and weather conditions. The proposed method can achieve less than 0.20 m mean localization errors. Compared with existing map-based methods, our method utilizes bi-sensors to improve localization performance.