Vertical Corner Feature Based Precise Vehicle Localization Using 3D LIDAR in Urban Area
Tall buildings are concentrated in urban areas. The outer walls of buildings are vertically erected to the ground and almost flat. Therefore, the vertical corners that meet the vertical planes are present everywhere in urban areas. These corners act as convenient landmarks, which can be extracted by...
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doaj-1c0bc21f99e64e2a84f02fa9d7a8fc802020-11-24T21:50:58ZengMDPI AGSensors1424-82202016-08-01168126810.3390/s16081268s16081268Vertical Corner Feature Based Precise Vehicle Localization Using 3D LIDAR in Urban AreaJun-Hyuck Im0Sung-Hyuck Im1Gyu-In Jee2Department of Electronic Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, KoreaSatellite Navigation Team, Korea Aerospace Research Institute, 169-84 Gwahak-ro, Yuseong-gu, Daejeon 305-806, KoreaDepartment of Electronic Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, KoreaTall buildings are concentrated in urban areas. The outer walls of buildings are vertically erected to the ground and almost flat. Therefore, the vertical corners that meet the vertical planes are present everywhere in urban areas. These corners act as convenient landmarks, which can be extracted by using the light detection and ranging (LIDAR) sensor. A vertical corner feature based precise vehicle localization method is proposed in this paper and implemented using 3D LIDAR (Velodyne HDL-32E). The vehicle motion is predicted by accumulating the pose increment output from the iterative closest point (ICP) algorithm based on the geometric relations between the scan data of the 3D LIDAR. The vertical corner is extracted using the proposed corner extraction method. The vehicle position is then corrected by matching the prebuilt corner map with the extracted corner. The experiment was carried out in the Gangnam area of Seoul, South Korea. In the experimental results, the maximum horizontal position error is about 0.46 m and the 2D Root Mean Square (RMS) horizontal error is about 0.138 m.http://www.mdpi.com/1424-8220/16/8/1268precise vehicle localizationvertical corner featureurban areacorner map3D LIDAR |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jun-Hyuck Im Sung-Hyuck Im Gyu-In Jee |
spellingShingle |
Jun-Hyuck Im Sung-Hyuck Im Gyu-In Jee Vertical Corner Feature Based Precise Vehicle Localization Using 3D LIDAR in Urban Area Sensors precise vehicle localization vertical corner feature urban area corner map 3D LIDAR |
author_facet |
Jun-Hyuck Im Sung-Hyuck Im Gyu-In Jee |
author_sort |
Jun-Hyuck Im |
title |
Vertical Corner Feature Based Precise Vehicle Localization Using 3D LIDAR in Urban Area |
title_short |
Vertical Corner Feature Based Precise Vehicle Localization Using 3D LIDAR in Urban Area |
title_full |
Vertical Corner Feature Based Precise Vehicle Localization Using 3D LIDAR in Urban Area |
title_fullStr |
Vertical Corner Feature Based Precise Vehicle Localization Using 3D LIDAR in Urban Area |
title_full_unstemmed |
Vertical Corner Feature Based Precise Vehicle Localization Using 3D LIDAR in Urban Area |
title_sort |
vertical corner feature based precise vehicle localization using 3d lidar in urban area |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2016-08-01 |
description |
Tall buildings are concentrated in urban areas. The outer walls of buildings are vertically erected to the ground and almost flat. Therefore, the vertical corners that meet the vertical planes are present everywhere in urban areas. These corners act as convenient landmarks, which can be extracted by using the light detection and ranging (LIDAR) sensor. A vertical corner feature based precise vehicle localization method is proposed in this paper and implemented using 3D LIDAR (Velodyne HDL-32E). The vehicle motion is predicted by accumulating the pose increment output from the iterative closest point (ICP) algorithm based on the geometric relations between the scan data of the 3D LIDAR. The vertical corner is extracted using the proposed corner extraction method. The vehicle position is then corrected by matching the prebuilt corner map with the extracted corner. The experiment was carried out in the Gangnam area of Seoul, South Korea. In the experimental results, the maximum horizontal position error is about 0.46 m and the 2D Root Mean Square (RMS) horizontal error is about 0.138 m. |
topic |
precise vehicle localization vertical corner feature urban area corner map 3D LIDAR |
url |
http://www.mdpi.com/1424-8220/16/8/1268 |
work_keys_str_mv |
AT junhyuckim verticalcornerfeaturebasedprecisevehiclelocalizationusing3dlidarinurbanarea AT sunghyuckim verticalcornerfeaturebasedprecisevehiclelocalizationusing3dlidarinurbanarea AT gyuinjee verticalcornerfeaturebasedprecisevehiclelocalizationusing3dlidarinurbanarea |
_version_ |
1725881278300946432 |