Automatic silo axis detection from RGB-D sensor data for content monitoring

Abstract

RGB-D sensors can be a low-cost solution for an accurate silo’s content monitoring which is fundamental for its efficient management. Some reference information such as the position and orientation of the sensor with respect to the silo’s geometry is fundamental for obtaining correct content measurements from acquired data. Since in real cases this information is not always known, a new method to obtain these measurements is proposed. This, taking as input sensor acquired data (represented as a point cloud), automatically computes the silo’s axis to provide a new reference system from which the point cloud can be easily processed. The z-axis of this reference system coincides with the gravity axis and the xy-plane is parallel to the ground plane. It is obtained in a six-step process that exploits the silo geometry properties and an estimation of the shape tensors of the acquired points. The method has been implemented and tested on both synthetic and real silos, considering a complete silo’s discharge process and different camera positions. Data acquired at each discharge has been transformed using the new reference system and compared with the silo’s ground truth (manually obtained for the real silos). To evaluate the accuracy the input point cloud to adjusted point cloud average distance has been considered. In all the tests, the well-performance of the proposal has been demonstrated, achieving a maximum average distance error of less than 6 cm