RESEARCH FOR SELF-POSITIONING CORRECTION AND GENERATION OF WIDE-AREA POINT CLOUD DATA USING SENSING UNIT MOUNTED ON CAR WITH MULTIPLE LIDAR SENSORS
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- TSUKADA Yoshinori
- 摂南大学 経営学部
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- NAKAHARA Masaya
- 大阪電気通信大学 総合情報学部
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- UMEHARA Yoshimasa
- 摂南大学 経営学部
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- NISHITA Yoshito
- 金沢工業大学 基礎教育部
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- KUBOTA Satoshi
- 関西大学 環境都市工学部
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- TANAKA Shigenori
- 関西大学 総合情報学部
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- KAWASAKI Yushi
- 関西大学大学院 総合情報学研究科
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- SANO Ryota
- 株式会社エイト日本技術開発
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- TANAKA Gou
- 株式会社長大
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- OTSUKI Shoji
- 株式会社日本インシーク/関西大学大学院 総合情報学研究科
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- INAMI Mao
- 株式会社パスコ
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- TAIRA Kenji
- 三菱電機エンジニアリング株式会社
Bibliographic Information
- Other Title
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- 複数LiDARによる車両搭載型センシングユニットを用いた自己位置補正と広域点群データの生成に関する研究
Abstract
<p> In recent years, point cloud data measured by mobile mapping systems (MMSs) haves been used for the maintenance and management of roadside structures. However, using MMSs to perform frequent routine inspections is not cost-effective. To this end, we previously developed a sensing unit mounted on a car using inexpensive sensors. A self-positioning correction method using simultaneous localizatioiin and mapping with multiple lidar detection and ranging (LiDAR) sensors installed horizontally and diagonally was investigated, and its usefulness was confirmed. However, the diagonal installation of LiDAR has a problem in that the accuracy of point cloud data generation deteriorates when the measurement data between consecutive points have the same shape. Additionally, it is necessary to superimpose multiple LiDAR point cloud data with different measurement ranges for a comprehensive measurement. Therefore, here, we propose a method to generate point cloud data over a wide area by using the self-position of the horizontally installed LiDAR to correct the self-position of the diagonally installed LiDAR and superimpose the point cloud data of both installation methods. In the result, the method was verified in some experiments, we confirmed it was useful.</p>
Journal
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- Japanese Journal of JSCE
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Japanese Journal of JSCE 79 (22), n/a-, 2023
Japan Society of Civil Engineers
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Details 詳細情報について
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- CRID
- 1390014093904489088
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- ISSN
- 24366021
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- Text Lang
- ja
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- Data Source
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- JaLC
- Crossref
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- Abstract License Flag
- Disallowed