2.5 Integrated Framework of Improved LeGO-LOAM and Enhanced
OpenPlanner
In previous sections, a unique hardware system tailored for the
greenhouse agricultural environment and enhanced localization and
navigation algorithms were proposed. In this section, we will introduce
the overall framework of autonomous navigation for greenhouse robots
based on these innovations. As shown in Fig. 2, the entire system can be
divided into two components: SLAM and navigation.
The implementation of SLAM is based on the Improved LeGO-LOAM. The
contribution of this paper includes the integration of a relocalization
module and the fusion of information from multiple sensors. These
enhancements help to achieve higher accuracy in pose and relocalization
information, as well as a high-quality 3D point cloud prior map. All of
these contribute to improving the accuracy of the system’s information
acquisition.
The navigation process consists of four modules, as detailed in Fig. 2.
Initially, a global trajectory is formulated using a dynamic programming
algorithm, dependent on the prior map and waypoints. Following this,
modules dedicated to environmental perception, local planning, and
trajectory tracking persistently execute until the designated target is
achieved. Of importance is the enhancement of the local planner,
contributing to an increased robustness in greenhouse navigation.
Table 2. Key Parameters of Experiment
Parameter | Value |
Lidar range | 0.15m-150m |
Lidar frequency | 10HZ |
LiDAR Scan Lines | 16 |
Traversable Height | 0.6m |
Mini speed of dynamic obstacle | 0.5 m/s |