Wu Yuanqing, Li Yanzhou, Li Wenhao, Li Hongyi, Lu Renquan
IEEE Trans Neural Netw Learn Syst. 2021 Dec;32(12):5633-5643. doi: 10.1109/TNNLS.2020.3027983. Epub 2021 Nov 30.
This article proposes a robust and precise localization scheme for unmanned ground vehicle (UGV) in global positioning system (GPS)-denied and GPS-challenged environments via multisensor fusion approach. The localization scheme is proposed to be under an available point-cloud map. First, initialization in localization module is designed to calculate the initial position of UGV in map using the Gaussian projection approach and obtain the frame transformation between the 3-D lidar and the inertial measurement unit (IMU). Second, the best alignment between each scan frame and the available submap is obtained, and the pose of vehicle relative to the origin of map is calculated. Third, the precise pose of UGV is well predicted by integrating the data from 3-D lidar and IMU. Fourth, in order to visually verify the proposed localization scheme, the motion of vehicle is visualized by designing the visualization module. Note that the preprocessing module aims to process the raw scan data. The availability of our proposed localization scheme is verified by conducting experiments in our campus.
本文提出了一种通过多传感器融合方法，在全球定位系统（GPS）信号缺失和受干扰环境下，用于无人地面车辆（UGV）的鲁棒且精确的定位方案。该定位方案是在可用点云地图下提出的。首先，定位模块中的初始化设计为使用高斯投影方法计算UGV在地图中的初始位置，并获得三维激光雷达与惯性测量单元（IMU）之间的帧变换。其次，获得每个扫描帧与可用子地图之间的最佳对齐，并计算车辆相对于地图原点的位姿。第三，通过融合三维激光雷达和IMU的数据，对UGV的精确位姿进行了很好的预测。第四，为了直观地验证所提出的定位方案，通过设计可视化模块对车辆的运动进行可视化。请注意，预处理模块旨在处理原始扫描数据。通过在我们校园内进行实验，验证了我们所提出定位方案的有效性。
