Multi-Modal Obstacle Detection and Evaluation of Occupancy Grid Mapping in Agriculture

In recent years, mapping and automation has been increasingly investigated and applied in precision agriculture. The ultimate goal of this development is to apply autonomous vehicles operating efficiently without any human intervention. Such autonomous operation imposes severe safety hazards, demanding accurate and robust risk detection, and avoidance systems. It is unlikely that one sensor can single­handedly guarantee this, and therefore multiple sensing modalities are often combined in order to increase detection performance and introduce redundancy. In this paper, we present a global mapping approach utilizing diverse sensor technologies to achieve a uniform obstacle interpretation of the environment. Using occupancy grid maps, we fuse information from a monocular color camera, a RADAR, and a LIDAR in combination with IMU­assisted GPS­positioning. For each sensor, we present detection algorithms, mapping from raw sensor data to a 2D grid­based obstacle interpretation of the environment. These are then fused temporally with the occupancy grid algorithm, and afterwards spatially in a competitive and complementary way to produce a combined global obstacle map. The method is evaluated on an extensive dataset recorded at Research Centre Foulum, Denmark, in June 2015. The dataset comprises sensor data from a tractor­mounted recording system in a grass mowing scenario with various obstacles. A ground truth map has been obtained with a mapping drone. Results show promising obstacle detection capabilities and an increase in performance when fusing information across sensor modalities and layers. The proposed mapping framework is able to fuse a vast amount of information across a diverse sensor set, using an efficient and novel approach for obstacle detection in agriculture.