Object Detection and Terrain Classification in Agricultural Fields Using 3D Lidar Data

Autonomous navigation and operation of agricultural vehicles is a challenging task due to the rather unstructured environment. An uneven terrain consisting of ground and vegetation combined with the risk of non-traversable obstacles necessitates a strong focus on safety and reliability. This paper presents an object detection and terrain classification approach for classifying individual points from 3D point clouds acquired using single multi-beam lidar scans. Using a support vector machine SVM classifier, individual 3D points are categorized as either ground, vegetation, or object based on features extracted from local neighborhoods. Experiments performed at a local working farm show that the proposed method has a combined classification accuracy of $$91.6\,\%$$, detecting points belonging to objects such as humans, animals, cars, and buildings with $$81.1\,\%$$ accuracy, while classifying vegetation with an accuracy of $$97.5\,\%$$.