Abstract
Self-localization is a crucial task for intelligent vehicles. Existing localization methods usually require high-cost IMU (Inertial Measurement Unit) or expensive LiDAR sensors (e.g., Velodyne HDL-64E). In this paper, we propose a low-cost yet accurate localization solution by using a custom-level GPS receiver and a low-cost camera with the support of HD map. Unlike existing HD map-based methods, which usually requires unique landmarks within the sensed range, the proposed method utilizes common lane lines for vehicle localization by using Kalman filter to fuse the GPS, monocular vision, and HD map for more accurate vehicle localization. In the Kalman filter framework, the observations consist of two parts. One is the raw GPS coordinate. The other is the lateral distance between the vehicle and the lane, which is computed from the monocular camera. The HD map plays the role of providing reference position information and correlating the local lateral distance from the vision and the GPS coordinates so as to formulate a linear Kalman filter. In the prediction step, we propose using a data-driven motion model rather than a Kinematic model, which is more adaptive and flexible. The proposed method has been tested with both simulation data and real data collected in the field. The results demonstrate that the localization errors from the proposed method are less than half or even one-third of the original GPS positioning errors by using low cost sensors with HD map support. Experimental results also demonstrate that the integration of the proposed method into existing ones can greatly enhance the localization results.
Highlights
Intelligent vehicles are emerging technologies that have great potential to enhance driving safety and improve transportation efficiency [1]
(1) We propose using linear Kalman filter to fuse GPS, monocular vision, and High Definition (HD) map and a low-cost and accurate solution to vehicle localization; developed a low-cost and accurate solution to vehicle localization; (2)
By referring to the HD map, we can we can correlate the lateral distance from monocular vision and GPS coordinates, and feed them as the correlate the lateral distance from monocular vision and GPS coordinates, and feed them as the measurements into a Kalman filter
Summary
Intelligent vehicles are emerging technologies that have great potential to enhance driving safety and improve transportation efficiency [1]. One state-of-the-art method was proposed by Apollo team by using a Velodyne HDL-64E They used LiDAR data for map generation and localized the vehicle by matching LiDAR data in cell-grid scale with 5–10 cm accuracy. Such method relies on the high-cost laser hardware (i.e., Velodyne HDL-64E). We proposed a new localization method for intelligent vehicles by integrating GPS, monocular vision, and HD map; with a low-cost monocular camera, a common GPS, and lane-level. The proposed method requires no high-cost devices, such as IMU or high definition Laser, and suggests a low-cost yet accurate solution to intelligent vehicle localization.
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