Road boundary detection is essential for autonomous vehicle localization and decision-making,especially under GPS signal loss and lane discontinuities.For road boundary detection in structural environments,obstacle oc...Road boundary detection is essential for autonomous vehicle localization and decision-making,especially under GPS signal loss and lane discontinuities.For road boundary detection in structural environments,obstacle occlusions and large road curvature are two significant challenges.However,an effective and fast solution for these problems has remained elusive.To solve these problems,a speed and accuracy tradeoff method for LiDAR-based road boundary detection in structured environments is proposed.The proposed method consists of three main stages:1)a multi-feature based method is applied to extract feature points;2)a road-segmentation-line-based method is proposed for classifying left and right feature points;3)an iterative Gaussian Process Regression(GPR)is employed for filtering out false points and extracting boundary points.To demonstrate the effectiveness of the proposed method,KITTI datasets is used for comprehensive experiments,and the performance of our approach is tested under different road conditions.Comprehensive experiments show the roadsegmentation-line-based method can classify left,and right feature points on structured curved roads,and the proposed iterative Gaussian Process Regression can extract road boundary points on varied road shapes and traffic conditions.Meanwhile,the proposed road boundary detection method can achieve real-time performance with an average of 70.5 ms per frame.展开更多
即时定位与地图构建(simultaneous localization and mapping,SLAM)算法是移动机器人实现自主移动的关键环节。激光雷达(LiDAR)具有测距精度高、不易受外部干扰和地图构建直观方便等优点,广泛应用于大型复杂室内外场景地图的构建。随着3...即时定位与地图构建(simultaneous localization and mapping,SLAM)算法是移动机器人实现自主移动的关键环节。激光雷达(LiDAR)具有测距精度高、不易受外部干扰和地图构建直观方便等优点,广泛应用于大型复杂室内外场景地图的构建。随着3D激光器的应用与普及,国内外学者围绕基于3D激光雷达的SLAM算法的研究已取得丰硕的成果。梳理了3D激光SLAM算法在前端数据关联、后端优化等环节的国内外研究现状,分析总结了目前各种3D激光SLAM算法以及改进方案的原理和优缺点,阐述了深度学习和多传感器融合理论与技术在3D激光SLAM算法中的应用情况,指出多源信息融合、与深度学习结合、应用场景的鲁棒性、SLAM算法通用框架及移动传感器和无线信号体制的技术渗透是3D激光SLAM算法的研究热点和发展趋势。研究成果对3D激光SLAM算法和未知环境中移动机器人即时定位和地图构建的研究具有重要的参考价值和指导意义。展开更多
基金国家自然科学基金重大研究计划项目:智能车驾驶脑认知技术平台与转化研究(91420202)Newton Fund Project:Talents Cultivation and Cooperation Oriented to Intelligent Vehicle Industrialization(UK-CIAPP\324)资助
基金This work was supported by the Research on Construction and Simulation Technology of Hardware in Loop Testing Scenario for Self-Driving Electric Vehicle in China(2018YFB0105103J).
文摘Road boundary detection is essential for autonomous vehicle localization and decision-making,especially under GPS signal loss and lane discontinuities.For road boundary detection in structural environments,obstacle occlusions and large road curvature are two significant challenges.However,an effective and fast solution for these problems has remained elusive.To solve these problems,a speed and accuracy tradeoff method for LiDAR-based road boundary detection in structured environments is proposed.The proposed method consists of three main stages:1)a multi-feature based method is applied to extract feature points;2)a road-segmentation-line-based method is proposed for classifying left and right feature points;3)an iterative Gaussian Process Regression(GPR)is employed for filtering out false points and extracting boundary points.To demonstrate the effectiveness of the proposed method,KITTI datasets is used for comprehensive experiments,and the performance of our approach is tested under different road conditions.Comprehensive experiments show the roadsegmentation-line-based method can classify left,and right feature points on structured curved roads,and the proposed iterative Gaussian Process Regression can extract road boundary points on varied road shapes and traffic conditions.Meanwhile,the proposed road boundary detection method can achieve real-time performance with an average of 70.5 ms per frame.
文摘即时定位与地图构建(simultaneous localization and mapping,SLAM)算法是移动机器人实现自主移动的关键环节。激光雷达(LiDAR)具有测距精度高、不易受外部干扰和地图构建直观方便等优点,广泛应用于大型复杂室内外场景地图的构建。随着3D激光器的应用与普及,国内外学者围绕基于3D激光雷达的SLAM算法的研究已取得丰硕的成果。梳理了3D激光SLAM算法在前端数据关联、后端优化等环节的国内外研究现状,分析总结了目前各种3D激光SLAM算法以及改进方案的原理和优缺点,阐述了深度学习和多传感器融合理论与技术在3D激光SLAM算法中的应用情况,指出多源信息融合、与深度学习结合、应用场景的鲁棒性、SLAM算法通用框架及移动传感器和无线信号体制的技术渗透是3D激光SLAM算法的研究热点和发展趋势。研究成果对3D激光SLAM算法和未知环境中移动机器人即时定位和地图构建的研究具有重要的参考价值和指导意义。
