Park Gwangsoo, Lee Byungjin, Sung Sangkyung
Department of Aerospace Information Engineering, Konkuk University, Seoul 05029, Korea.
Sensors (Basel). 2021 Aug 23;21(16):5670. doi: 10.3390/s21165670.
Point cloud data is essential measurement information that has facilitated an extended functionality horizon for urban mobility. While 3D lidar and image-depth sensors are superior in implementing mapping and localization, sense and avoidance, and cognitive exploration in an unknown area, applying 2D lidar is inevitable for systems with limited resources of weight and computational power, for instance, in an aerial mobility system. In this paper, we propose a new pose estimation scheme that reflects the characteristics of extracted feature point information from 2D lidar on the NDT framework for exploiting an improved point cloud registration. In the case of the 2D lidar point cloud, vertices and corners can be viewed as representative feature points. Based on this feature point information, a point-to-point relationship is functionalized and reflected on a voxelized map matching process to deploy more efficient and promising matching performance. In order to present the navigation performance of the mobile object to which the proposed algorithm is applied, the matching result is combined with the inertial navigation through an integration filter. Then, the proposed algorithm was verified through a simulation study using a high-fidelity flight simulator and an indoor experiment. For performance validation, both results were compared and analyzed with the previous techniques. In conclusion, it was demonstrated that improved accuracy and computational efficiency could be achieved through the proposed algorithms.
点云数据是至关重要的测量信息，它为城市交通拓展了功能视野。虽然三维激光雷达和图像深度传感器在实现地图绘制与定位、感知与避障以及未知区域的认知探索方面具有优势，但对于重量和计算能力资源有限的系统，例如空中交通系统，应用二维激光雷达是不可避免的。在本文中，我们提出了一种新的姿态估计方案，该方案在NDT框架上反映了从二维激光雷达提取的特征点信息的特征，以利用改进的点云配准。对于二维激光雷达点云，顶点和角点可被视为代表性特征点。基于此特征点信息，点对点关系被功能化并反映在体素地图匹配过程中，以实现更高效且有前景的匹配性能。为了展示应用所提算法的移动对象的导航性能，匹配结果通过积分滤波器与惯性导航相结合。然后，通过使用高保真飞行模拟器的仿真研究和室内实验对所提算法进行了验证。为了进行性能验证，将这两个结果与先前技术进行了比较和分析。总之，结果表明所提算法能够实现更高的精度和计算效率。
