Autonomous navigation system in a greenhouse using LIO-SAM mapping and laser vision fusion localization

Navigation challenges have been posed on the conventional systems in greenhouses, such as significant light variations, narrow crop row spacing, failure to receive satellite signals, and rigid travel paths. In this study, autonomous navigation was proposed to integrate laser vision with 3D SLAM (simultaneous localization and mapping). Environmental data was collected from 3D LIDAR VLP-16 (Velodyne LiDAR) and an IMU (inertial measurement unit). The LIO-SAM (tightly-coupled lidar inertial odometry via smoothing and mapping) was employed to generate 3D point cloud maps, which were subsequently downscaled to the raster maps. This integration included the data from wheeled odometers and visual odometers using an Extended Kalman filter. Visual odometers provided the positional information to correct and update the state prediction of the mobile platform, functioning as a local localization tool. Additionally, the adaptive Monte Carlo localization data introduced the weights to the ndt-matching (normal distributions transform matching), in order to enhance the accuracy of global localization. Moreover, the autonomous walking system was utilized as the A* algorithm and dynamic window algorithm for the path creation and autonomous navigation. The navigation system of autonomous walking was composed of a remote monitoring platform and an on-board system. Specifically, the remote monitoring platform was responsible for selecting the working mode of the onboard system, then releasing the instruction of target points, and finally displaying the location. The on-board system was the executor of the instructions, in order to receive and execute the task instructions ordered by the monitoring platform. The remote monitoring and onboard systems were combined to realize the autonomous navigation task of the greenhouse transportation robot, according to real-time communication through a wireless network. Experimental results showed that the maximum relative error, the maximum absolute error, and the root mean square error of the greenhouse navigation map constructed by the LIO-SAM algorithm reached 9.9%, 0.081 and 0.063 m, respectively. The improved localization algorithm reduced the horizontal and vertical deviations in the autonomous walking system (less than 0.020 and 0.090 m, respectively). The new system maintained mean values of horizontal deviation, longitudinal deviation, and heading declination below 0.120 m, 0.100 m, and 8.5°, respectively, with standard deviations of less than 0.070 m, 0.140 m, and 6.6°, respectively. The approach had significantly improved the accuracy of positioning and navigation. This navigation scheme can fulfill the need for high-precision localization and navigation in the autonomous walking systems within the greenhouse. The findings can provide theoretical and technical support to autonomous mobile platforms.