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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Main Authors: Jun-Hyuck Im, Sung-Hyuck Im, Gyu-In Jee
Format: Article
Language:English
Published: MDPI AG 2016-08-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/16/8/1268
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spelling 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
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AT sunghyuckim verticalcornerfeaturebasedprecisevehiclelocalizationusing3dlidarinurbanarea
AT gyuinjee verticalcornerfeaturebasedprecisevehiclelocalizationusing3dlidarinurbanarea
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