Abstract
There are numerous global navigation satellite system-denied regions in urban areas, where the localization of autonomous driving remains a challenge. To address this problem, a high-resolution light detection and ranging (LiDAR) sensor was recently developed. Various methods have been proposed to improve the accuracy of localization using precise distance measurements derived from LiDAR sensors. This study proposes an algorithm to accelerate the computational speed of LiDAR localization while maintaining the original accuracy of lightweight map-matching algorithms. To this end, first, a point cloud map was transformed into a normal distribution (ND) map. During this process, vector-based normal distribution transform, suitable for graphics processing unit (GPU) parallel processing, was used. In this study, we introduce an algorithm that enabled GPU parallel processing of an existing ND map-matching process. The performance of the proposed algorithm was verified using an open dataset and simulations. To verify the practical performance of the proposed algorithm, the real-time serial and parallel processing performances of the localization were compared using high-performance and embedded computers, respectively. The distance root-mean-square error and computational time of the proposed algorithm were compared. The algorithm increased the computational speed of the embedded computer almost 100-fold while maintaining high localization precision.
Highlights
Since the mid-1980s, remarkable research efforts and investments have been devoted to developing autonomous driving technology [1,2]
Compared with the reference ego pose, VNDT demonstrated the highest precision among the localization algorithms
ICP had a constant bias, and GNDT was adversely affected by noise
Summary
Since the mid-1980s, remarkable research efforts and investments have been devoted to developing autonomous driving technology [1,2]. Automotive companies have dedicated remarkable efforts to the commercialization of this technology, which requires near-perfect safety conditions [3–5]. Precise real-time perception of the surrounding environment using various sensors, such as a global navigation satellite system (GNSS), cameras, light detection and ranging (LiDAR) equipment, radio detection and ranging (RADAR) technology, and the inertial measurement unit (IMU), is essential [6]. Diverse types of information are required for safe autonomous driving, including the location of the ego vehicle, road environment, and spatial relationship between the surrounding objects. As most perception methods are performed under the assumption that the pose of the ego vehicle is accurately known, inaccurate pose information may result in the deterioration of the performance of the autonomous driving system
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