Long Zhenhuan, Xiang Yang, Lei Xiangming, Li Yajun, Hu Zhengfang, Dai Xiufeng
College of Mechanical and Electrical Engineering, Hunan Agriculture University, Changsha 410128, China.
Sensors (Basel). 2022 Jun 25;22(13):4819. doi: 10.3390/s22134819.
Conventional mobile robots employ LIDAR for indoor global positioning and navigation, thus having strict requirements for the ground environment. Under the complicated ground conditions in the greenhouse, the accumulative error of odometer (ODOM) that arises from wheel slip is easy to occur during the long-time operation of the robot, which decreases the accuracy of robot positioning and mapping. To solve the above problem, an integrated positioning system based on UWB (ultra-wideband)/IMU (inertial measurement unit)/ODOM/LIDAR is proposed. First, UWB/IMU/ODOM is integrated by the Extended Kalman Filter (EKF) algorithm to obtain the estimated positioning information. Second, LIDAR is integrated with the established two-dimensional (2D) map by the Adaptive Monte Carlo Localization (AMCL) algorithm to achieve the global positioning of the robot. As indicated by the experiments, the integrated positioning system based on UWB/IMU/ODOM/LIDAR effectively reduced the positioning accumulative error of the robot in the greenhouse environment. At the three moving speeds, including 0.3 m/s, 0.5 m/s, and 0.7 m/s, the maximum lateral error is lower than 0.1 m, and the maximum lateral root mean square error (RMSE) reaches 0.04 m. For global positioning, the RMSEs of the x-axis direction, the y-axis direction, and the overall positioning are estimated as 0.092, 0.069, and 0.079 m, respectively, and the average positioning time of the system is obtained as 72.1 ms. This was sufficient for robot operation in greenhouse situations that need precise positioning and navigation.
传统移动机器人采用激光雷达进行室内全局定位和导航，因此对地面环境有严格要求。在温室复杂的地面条件下，机器人长时间运行时容易出现因车轮打滑而产生的里程计（ODOM）累积误差，这会降低机器人定位和建图的精度。为解决上述问题，提出了一种基于超宽带（UWB）/惯性测量单元（IMU）/里程计（ODOM）/激光雷达的集成定位系统。首先，通过扩展卡尔曼滤波器（EKF）算法将UWB/IMU/ODOM进行集成，以获得估计的定位信息。其次，利用自适应蒙特卡洛定位（AMCL）算法将激光雷达与已建立的二维（2D）地图进行集成，以实现机器人的全局定位。实验表明，基于UWB/IMU/ODOM/激光雷达的集成定位系统有效降低了机器人在温室环境中的定位累积误差。在0.3 m/s、0.5 m/s和0.7 m/s这三种移动速度下，最大横向误差低于0.1 m，最大横向均方根误差（RMSE）达到0.04 m。对于全局定位，x轴方向、y轴方向和整体定位的RMSE分别估计为0.092、0.069和0.079 m，系统的平均定位时间为72.1 ms。这对于需要精确定位和导航的温室环境中的机器人操作来说已经足够。
