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作者:

Duan, Jianmin (Duan, Jianmin.) (学者:段建民) | Shi, Lixiao (Shi, Lixiao.) | Yao, Junqin (Yao, Junqin.) | Liu, Dan (Liu, Dan.) | Tian, Qi (Tian, Qi.)

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CPCI-S

摘要:

In order to complete the task of vehicle's autonomous driving in structural road, it's necessary to detect obstacles. This paper proposed a method of obstacle detection, taking use of the four-line laser radar. This algorithm combined the improved DBSCAN with K-Means and overcame DBSCAN's defect that it couldn't divide obstacles with similar density. At the same time, this method can eliminate noise points effectively. Based on clustering, obstacle's information can be acquired, such as angle, distance and size, to achieve the task of obstacle detection. The algorithm is applied to obstacle detection in intelligent vehicle. The test proved that it is consistent and reliable, which complies with the requirement of intelligent vehicle's autonomous driving.

关键词:

DBSCAN Intelligent vehicle K-means Laser radar Obstacle detection

作者机构:

  • [ 1 ] [Duan, Jianmin]Beijing Univ Technol, Dept Control Sci & Engn, Measurement Control Syst & Equipment Grp, Beijing 100022, CO, Peoples R China
  • [ 2 ] [Shi, Lixiao]Beijing Univ Technol, Dept Control Sci & Engn, Measurement Control Syst & Equipment Grp, Beijing 100022, CO, Peoples R China
  • [ 3 ] [Yao, Junqin]Beijing Univ Technol, Dept Control Sci & Engn, Measurement Control Syst & Equipment Grp, Beijing 100022, CO, Peoples R China
  • [ 4 ] [Liu, Dan]Beijing Univ Technol, Dept Control Sci & Engn, Measurement Control Syst & Equipment Grp, Beijing 100022, CO, Peoples R China
  • [ 5 ] [Tian, Qi]Beijing Univ Technol, Dept Control Sci & Engn, Measurement Control Syst & Equipment Grp, Beijing 100022, CO, Peoples R China

通讯作者信息:

  • 段建民

    [Duan, Jianmin]Beijing Univ Technol, Dept Control Sci & Engn, Measurement Control Syst & Equipment Grp, Beijing 100022, CO, Peoples R China

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来源 :

2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO)

年份: 2013

页码: 2452-2457

语种: 英文

被引次数:

WoS核心集被引频次: 4

SCOPUS被引频次:

ESI高被引论文在榜: 0 展开所有

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