Integrated Indoor Positioning System of Greenhouse Robot Based on UWB/IMU/ODOM/LIDAR

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 13; p. 4819
• Main Authors: Long, Zhenhuan, Xiang, Yang, Lei, Xiangming, Li, Yajun, Hu, Zhengfang, Dai, Xiufeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 25.06.2022; MDPI
• Subjects: Accuracy; Algorithms; Cameras; Extended Kalman filter; greenhouse; Greenhouses; indoor positioning; Inertial platforms; Robots; Root-mean-square errors; Sensors; Ultrawideband; UWB/IMU/ODOM/LIDAR; Vision systems
• ISSN: 1424-8220; 1424-8220
• 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.