测绘通报 ›› 2020, Vol. 0 ›› Issue (11): 55-60.doi: 10.13474/j.cnki.11-2246.2020.0354

• 学术研究 • 上一篇    下一篇

基于复合多属性决策的点云匹配定位结果准确性评价

吴昱晗, 李朝阳, 薛庆全, 郄晓斌, 张亚博   

  1. 北京航天发射技术研究所, 北京 100076
  • 收稿日期:2020-01-15 出版日期:2020-11-25 发布日期:2020-11-30
  • 通讯作者: 李朝阳。E-mail:beijinglzy@163.com E-mail:beijinglzy@163.com
  • 作者简介:吴昱晗(1995-),女,硕士生,主要研究方向为导航制导与控制。E-mail:472631101@qq.com

Method for evaluating the accuracy of point cloud matching positioning results based on composite multi-attribute decision making

WU Yuhan, LI Chaoyang, XUE Qingquan, QIE Xiaobin, ZHANG Yabo   

  1. Beijing Institute of Space Launch Technology, Beijing 100076, China
  • Received:2020-01-15 Online:2020-11-25 Published:2020-11-30

摘要: 激光雷达点云匹配与惯性组合导航技术是高精度定位导航领域研究的热点,匹配定位结果评价的准确性直接影响组合导航定位精度,特别是在室外实际道路场景下,环境复杂,点云误匹配概率较大,只有准确评价匹配定位结果,才能提高组合导航定位精度。因此,对于评价模型的研究具有重要的意义。本文将评价模型的构建抽象为多属性决策问题,采用复合多属性决策算法,识别影响定位结果的因素并进行赋权,定量地评价了定位结果的质量,并利用实际道路数据对模型进行了验证。结果表明,本文算法可靠有效,能够反映实际匹配定位结果质量。

关键词: 多属性决策, 组合定位, 评价模型, 定位精度, 点云匹配

Abstract: LiDAR point cloud matching and inertial integrated navigation technology are hot topics in the field of high-precision positioning and navigation. The accuracy in the evaluation in matching positioning results directly affects the accuracy of integrated navigation positioning. Especially, outdoor actual road scenarios, the environment is complex, so the probability of mismatches is high. Only if evaluating the matching positioning results accurately can improve the integrated navigation positioning accuracy. Therefore, it is great significante to study evaluation models. In this paper, the construction of the evaluation model is abstracted as a multi-attribute decision problem. A compound multi-attribute decision algorithm is used to identify factors that influence the positioning results and give a weight. The quality of the positioning results is evaluated quantitatively, and the model is verified with using actual road data. The results show that the algorithm is reliable and effective, could reflect the quality of mateling positioning results actually.

Key words: multi-attribute decision making, integrated positioning, evaluation model, positioning precision, point clould matehing

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