Paper Info
Title
Extended Line Map-Based Precise Vehicle Localization Using 3D LIDAR.
Abstract
An Extended Line Map (ELM)-based precise vehicle localization method is proposed in this paper, and is implemented using 3D Light Detection and Ranging (LIDAR). A binary occupancy grid map in which grids for road marking or vertical structures have a value of 1 and the rest have a value of 0 was created using the reflectivity and distance data of the 3D LIDAR. From the map, lines were detected using a Hough transform. After the detected lines were converted into the node and link forms, they were stored as a map. This map is called an extended line map, of which data size is extremely small (134 KB/km). The ELM-based localization is performed through correlation matching. The ELM is converted back into an occupancy grid map and matched to the map generated using the current 3D LIDAR. In this instance, a Fast Fourier Transform (FFT) was applied as the correlation matching method, and the matching time was approximately 78 ms (based on MATLAB). The experiment was carried out in the Gangnam area of Seoul, South Korea. The traveling distance was approximately 4.2 km, and the maximum traveling speed was approximately 80 km/h. As a result of localization, the root mean square (RMS) position errors for the lateral and longitudinal directions were 0.136 m and 0.223 m, respectively.
Year
DOI
Venue
2018
10.3390/s18103179
SENSORS
Keywords
Field
DocType
extended line map,precise vehicle localization,3D LIDAR,road marking,vertical structure
MATLAB,Remote sensing,Hough transform,Electronic engineering,Lidar,Ranging,Fast Fourier transform,Root mean square,Engineering,Binary number,Occupancy grid mapping
Journal
Volume
Issue
ISSN
18
10.0
1424-8220
Citations 
PageRank 
References 
3
0.38
8
Authors
3
Name
Order
Citations
PageRank
Jun-Hyuck Im1141.44
Sunghyuck Im2333.28
Gyu-In Jee3869.72