Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2022 Sep 29;22(19):7424. doi: 10.3390/s22197424.
Unmanned ground vehicles (UGVs) are making more and more progress in many application scenarios in recent years, such as exploring unknown wild terrain, working in precision agriculture and serving in emergency rescue. Due to the complex ground conditions and changeable surroundings of these unstructured environments, it is challenging for these UGVs to obtain robust and accurate state estimations by using sensor fusion odometry without prior perception and optimization for specific scenarios. In this paper, based on an error-state Kalman filter (ESKF) fusion model, we propose a robust lidar-inertial odometry with a novel ground condition perception and optimization algorithm specifically designed for UGVs. The probability distribution gained from the raw inertial measurement unit (IMU) measurements during a certain time period and the state estimation of ESKF were both utilized to evaluate the flatness of ground conditions in real-time; then, by analyzing the relationship between the current ground condition and the accuracy of the state estimation, the tightly coupled lidar-inertial odometry was dynamically optimized further by adjusting the related parameters of the processing algorithm of the lidar points to obtain robust and accurate ego-motion state estimations of UGVs. The method was validated in various types of environments with changeable ground conditions, and the robustness and accuracy are shown through the consistent accurate state estimation in different ground conditions compared with the state-of-art lidar-inertial odometry systems.
近年来，无人地面车辆（UGV）在许多应用场景中取得了越来越多的进展，例如探索未知的野外地形、在精准农业中工作以及在紧急救援中服务。由于这些非结构化环境的地面条件复杂且周围环境不断变化，这些 UGV 很难通过使用传感器融合里程计进行鲁棒和准确的状态估计，而无需针对特定场景进行预先感知和优化。在本文中，我们基于误差状态卡尔曼滤波器（ESKF）融合模型，提出了一种针对 UGV 设计的具有新颖地面条件感知和优化算法的鲁棒激光惯性里程计。利用在特定时间段内从原始惯性测量单元（IMU）测量中获得的概率分布和 ESKF 的状态估计来实时评估地面条件的平坦度；然后，通过分析当前地面条件与状态估计准确性之间的关系，通过调整激光点处理算法的相关参数，进一步动态优化紧耦合激光惯性里程计，从而获得 UGV 鲁棒和准确的自身运动状态估计。该方法在具有变化地面条件的各种类型的环境中进行了验证，并通过与先进的激光惯性里程计系统相比，在不同地面条件下始终准确的状态估计展示了其鲁棒性和准确性。
