针对地下车库场景中SLAM定位不准的问题,文章提出一种基于视觉和雷达信息融合的SLAM算法,将三维ICP算法IMLS-ICP(Implicit Moving Least Square-Iterative Closest Point)进行二维化,并融入入PL-ICP(Point to Line-Iterative Closest Po...针对地下车库场景中SLAM定位不准的问题,文章提出一种基于视觉和雷达信息融合的SLAM算法,将三维ICP算法IMLS-ICP(Implicit Moving Least Square-Iterative Closest Point)进行二维化,并融入入PL-ICP(Point to Line-Iterative Closest Point)和N-ICP(Normal-Iterative Closest Point)的内容,使得IMLS-ICP更加适合于二维环境,获得更好的点云匹配效果。然后,从图像中提取ORB(Oriented FAST and Rotated BRIEF)特征点进行特征匹配,并针对于地下车库场景亮度不稳定的特点,对图像进行了直方图均衡化滤波处理,并用BRIEF(Binary Robust Independent Elementary Feature)描述子进行约束,使得ORB特征点的提取与匹配更具精确性和鲁棒性,并对得到的结果进行了局部的BA(Bundle Adjustment)优化。应用加权最小二乘法进行位姿融合。通过实验对位姿进行了分析,与主流2DSLAM算法进行对比,验证文章算法的有效性。展开更多
同步定位与地图构建(Simultaneous Location and Mapping,SLAM)是机器人在未知环境中实现自我导航能力的重要保证。目前SLAM算法使用的主传感器基本是激光雷达或视觉相机。二者各具优劣,激光雷达能更精确地进行测距,视觉相机能反映环境...同步定位与地图构建(Simultaneous Location and Mapping,SLAM)是机器人在未知环境中实现自我导航能力的重要保证。目前SLAM算法使用的主传感器基本是激光雷达或视觉相机。二者各具优劣,激光雷达能更精确地进行测距,视觉相机能反映环境丰富的纹理信息。与使用单一传感器相比,将二者融合的SLAM算法能够获得更多环境信息,达到更好的定位和建图效果。文章提出一种融合激光雷达和视觉相机的帧间匹配方法,通过在SLAM帧间匹配过程中加入地面约束以及视觉特征约束,提高帧间匹配过程精度,增强算法鲁棒性,从而提升SLAM算法整体效果。文章最后利用采集的地下停车库数据进行结果验证,与开源算法A-LOAM进行对比。结果表明,相比A-LOAM的帧间匹配方法,文章提出的方法相对位姿误差提升约30%。展开更多
In order to meet the application requirements of autonomous vehicles, this paper proposes a simultaneous localization and mapping （SLAM） algorithm, which uses a VoxelGrid filter to down sample the point cloud data, ...In order to meet the application requirements of autonomous vehicles, this paper proposes a simultaneous localization and mapping （SLAM） algorithm, which uses a VoxelGrid filter to down sample the point cloud data, with the combination of iterative closest points （ICP） algorithm and Gaussian model for particles updating, the matching between the local map and the global map to quantify particles＇ importance weight. The crude estimation by using ICP algorithm can find the high probability area of autonomous vehicles＇ poses, which would decrease particle numbers, increase algorithm speed and restrain particles＇ impoverishment. The calculation of particles＇ importance weight based on matching of attribute between grid maps is simple and practicable. Experiments carried out with the autonomous vehicle platform validate the effectiveness of our approaches.展开更多
激光雷达(light detection and ranging,LiDAR)同时定位与建图(simultaneous localization and mapping,SLAM)中的位姿估计依赖于高精度和高可靠性的扫描匹配算法。针对实时LiDAR里程计与建图(LiDAR odometry and mapping in real-time,...激光雷达(light detection and ranging,LiDAR)同时定位与建图(simultaneous localization and mapping,SLAM)中的位姿估计依赖于高精度和高可靠性的扫描匹配算法。针对实时LiDAR里程计与建图(LiDAR odometry and mapping in real-time,LOAM)框架中的点到线和点到面的迭代最近点算法(iterative closest point,ICP)在非结构化场景中退化的问题,提出了用于识别非结构化场景的环境特征值(environmental feature values,EFV),并根据EFV弹性地选择用正态分布变换(normal distributions transform,NDT)进行粗配准,实现了一种基于扫描匹配的弹性实时激光SLAM算法NDT-LOAM。实验结果表明,EFV可以有效区分非结构化场景,并给出了EFV阈值的调试方法。定位与建图实验分析表明,所提算法相比LOAM等经典的纯激光SLAM算法,在精度以及可靠性上均有较大提升,室外定位精度可从米级提升至分米级,在面对手持数据时也不会建图失败,能够得到全局一致性地图。因此此算法具有很好的环境适应性,丰富和发展了面向复杂环境的SLAM方法。展开更多
文摘同步定位与地图构建(Simultaneous Location and Mapping,SLAM)是机器人在未知环境中实现自我导航能力的重要保证。目前SLAM算法使用的主传感器基本是激光雷达或视觉相机。二者各具优劣,激光雷达能更精确地进行测距,视觉相机能反映环境丰富的纹理信息。与使用单一传感器相比,将二者融合的SLAM算法能够获得更多环境信息,达到更好的定位和建图效果。文章提出一种融合激光雷达和视觉相机的帧间匹配方法,通过在SLAM帧间匹配过程中加入地面约束以及视觉特征约束,提高帧间匹配过程精度,增强算法鲁棒性,从而提升SLAM算法整体效果。文章最后利用采集的地下停车库数据进行结果验证,与开源算法A-LOAM进行对比。结果表明,相比A-LOAM的帧间匹配方法,文章提出的方法相对位姿误差提升约30%。
基金Supported by the Major Research Plan of the National Natural Science Foundation of China(91120003)Surface Project of the National Natural Science Foundation of China(61173076)
文摘In order to meet the application requirements of autonomous vehicles, this paper proposes a simultaneous localization and mapping （SLAM） algorithm, which uses a VoxelGrid filter to down sample the point cloud data, with the combination of iterative closest points （ICP） algorithm and Gaussian model for particles updating, the matching between the local map and the global map to quantify particles＇ importance weight. The crude estimation by using ICP algorithm can find the high probability area of autonomous vehicles＇ poses, which would decrease particle numbers, increase algorithm speed and restrain particles＇ impoverishment. The calculation of particles＇ importance weight based on matching of attribute between grid maps is simple and practicable. Experiments carried out with the autonomous vehicle platform validate the effectiveness of our approaches.
文摘激光雷达(light detection and ranging,LiDAR)同时定位与建图(simultaneous localization and mapping,SLAM)中的位姿估计依赖于高精度和高可靠性的扫描匹配算法。针对实时LiDAR里程计与建图(LiDAR odometry and mapping in real-time,LOAM)框架中的点到线和点到面的迭代最近点算法(iterative closest point,ICP)在非结构化场景中退化的问题,提出了用于识别非结构化场景的环境特征值(environmental feature values,EFV),并根据EFV弹性地选择用正态分布变换(normal distributions transform,NDT)进行粗配准,实现了一种基于扫描匹配的弹性实时激光SLAM算法NDT-LOAM。实验结果表明,EFV可以有效区分非结构化场景,并给出了EFV阈值的调试方法。定位与建图实验分析表明,所提算法相比LOAM等经典的纯激光SLAM算法,在精度以及可靠性上均有较大提升,室外定位精度可从米级提升至分米级,在面对手持数据时也不会建图失败,能够得到全局一致性地图。因此此算法具有很好的环境适应性,丰富和发展了面向复杂环境的SLAM方法。
