On Multilevel Road Mapping for Autonomous Vehicles: A Study to Generate Accurate 2.5D LIDAR Maps Using Graph SLAM in Challenging Environments

自动驾驶车辆的多级道路测绘:在具有挑战性的环境中使用图 SLAM 生成精确 2.5D LIDAR 地图的研究

基本信息

  • 批准号:
    22K17974
  • 负责人:
  • 金额:
    $ 2.91万
  • 依托单位:
  • 依托单位国家:
    日本
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
  • 财政年份:
    2022
  • 资助国家:
    日本
  • 起止时间:
    2022-04-01 至 2024-03-31
  • 项目状态:
    已结题

项目摘要

I. A unique tactic to automatically map multilevel environments and localize autonomous vehicles accordingly in each layer has been reported in a Journal paper (Sensor: Remote Sensing).II.Many collected mapping data were analyzed to determine the challenging environments to generate accurate maps using GNSS/INS-RTK systems and investigate the reasons. This study was very important to be a reference to investigate and collect mapping data in the future. High buildings, dense, trees, long tunnels, multilayer junctions, bridges and underpasses were found to be the most critical road structures to deflect and obstruct the satellite signals received by GNSS/INS-RTK systems. Accordingly, the importance of generating accurate maps in multilevel road structures has been emphasized and the study and relevant analysis have been reported in a Journal paper (Sensor: Remote Sensing). III. A unique strategy to fix the global position inconsistency in the XY plane between road layers has been highlighted at The 25th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2022). IV. The capability to precisely combine/merge maps in the XY plane using the proposed Graph SLAM framework has been analyzed and integrated into the mapping module. The relevant results have been published in The 2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM).
I.一个独特的策略,自动映射多级环境和本地化自动驾驶车辆相应地在每一层已在期刊论文(传感器:遥感)。II.许多收集的映射数据进行了分析,以确定具有挑战性的环境,使用GNSS/INS-RTK系统生成准确的地图,并调查原因。本研究对今后的测绘资料调查和收集具有重要的参考价值。高层建筑、密集的树木、长隧道、多层交叉路口、桥梁和地下通道被认为是偏转和阻碍全球导航卫星系统/惯性导航卫星实时定位系统接收的卫星信号的最关键的道路结构。因此,在多级道路结构中生成准确地图的重要性得到了强调,并且在期刊论文(Sensor:Remote Sensing)中报告了研究和相关分析。三.第25届IEEE智能交通系统国际会议(IEEE ITSC 2022)强调了一种解决道路层之间XY平面中全局位置不一致的独特策略。四.已经分析了使用所提出的图形SLAM框架在XY平面中精确联合收割机/合并地图的能力,并将其集成到地图绘制模块中。相关成果已发表在2022年IEEE/ASME先进智能机电一体化国际会议(AIM)上。

项目成果

期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
2.5D Layered Sub-Image LIDAR Maps for Autonomous Driving in Multilevel Environments
用于多级环境中自动驾驶的 2.5D 分层子图像激光雷达地图
  • DOI:
    10.3390/rs14225847
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    田澤龍之介;鳥居拓馬;日高昇平;Mohammad Aldibaja and Noaki Suganuma and Reo Yanase
  • 通讯作者:
    Mohammad Aldibaja and Noaki Suganuma and Reo Yanase
On Mapping in Multilayer Environments: A Robust Graph-SLAM Approach Using LIDAR Intensity and Elevation Data
多层环境中的地图绘制:使用 LIDAR 强度和高程数据的鲁棒图 SLAM 方法
  • DOI:
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Mohammad Aldibaja;Reo Yanase and Naoki Suganuma
  • 通讯作者:
    Reo Yanase and Naoki Suganuma
On LIDAR Map Combination: A Graph Slam Module to Generate Accurate and Largescale Maps for Autonomous Driving
论LIDAR地图组合:为自动驾驶生成精确大比例地图的Graph Slam模块
  • DOI:
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Mohammad Aldibaja;Naoki Suganuma;Reo Yanase
  • 通讯作者:
    Reo Yanase
Challenging Environments for Precise Mapping Using GNSS/INS-RTK Systems: Reasons and Analysis
  • DOI:
    10.3390/rs14164058
  • 发表时间:
    2022-08
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Mohammad Aldibaja;N. Suganuma;Keisuke Yoneda;R. Yanase
  • 通讯作者:
    Mohammad Aldibaja;N. Suganuma;Keisuke Yoneda;R. Yanase
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