Obstacle Detection Using 3D-LIDAR during Rice Harvesting Operation

Abstract

In order to operate autonomous combine harvesters in rice fields, the harvester needs to detect obstacles on its path. One important such obstacle is a human. This research develops a 3D-LIDAR based method to detect and distinguish humans from other environmental objects, such as rice plants and the ground. Distance and intensity values from the 3D-LIDAR sensor were used for this detection. Once this data was captured, a filter was applied to mask rice plants and the ground, and then the remaining points plotted on a grid map. Evaluation of the resulting obstacle detection and collision avoidance system were undertaken during rice harvesting experiments. It was able to detect and respond to various obstacle (a human standing and sitting outside of the rice field, a human standing inside of the rice field, and a PVC pole) at the rate of 2.0 fps during harvesting.