Development of a LiDAR-based framework for obstacle identification and mapping in orchard environments

Abstract

The motion of a robot in the orchard environment is based on the identification of obstacles in the orchard environment. To achieve this, the robot should identify the obstacles and navigate away from such obstacles. This study proposes a method using LiDAR data to identify obstacles and process the location of all the obstacles. The method comprises of three key components: identification and segmentation of apple canopy features, identification of obstacles and trunks, mapping of the location of all obstacles and trunks. Firstly, the canopy features are identified based on the criteria; height, density and intensity. The identified canopy is bounded by voxels that delineate the boundaries of the canopy. Secondly, the obstacles and trunks are mapped, resulting in a guideline for the navigation of the orchard robot. The experimental results show that the canopy feature removal reduced processing time by 63.7 % compared to processing the full point cloud and was 42.3 % faster than Euclidean clustering method, significantly lowering computational demands. The mapping of trunks achieved an accuracy of 92.3 % by locating all the positions of the apple trunks. The experimental results yielded an average mapping error of 0.034m. The proposed algorithm in this study generates an orchard map of trunk features enabling precision agriculture applications such as yield prediction which is achieved through trunk canopy correlation and autonomous navigation.