A fast 3D surface reconstruction and volume estimation method for grain storage based on priori model

Inventory checking is one of the most significant parts for grain reserves, and plays a very important role on the macro-control of food and food security. Simple, fast and accurate method to obtain internal structure information and further to estimate the volume of the grain storage is needed. Here in our developed system, a special designed multi-site laser scanning system is used to acquire the range data clouds of the internal structure of the grain storage. However, due to the seriously uneven distribution of the range data, this data should firstly be preprocessed by an adaptive re-sampling method to reduce the data redundancy as well as noise. Then the range data is segmented and useful features, such as plane and cylinder information, are extracted. With these features a coarse registration between all of these single-site range data is done, and then an Iterative Closest Point (ICP) algorithm is carried out to achieve fine registration. Taking advantage of the structure of the grain storage being well defined and the types of them are limited, a fast automatic registration method based on the priori model is proposed to register the multi-sites range data more efficiently. Then after the integration of the multi-sites range data, the grain surface is finally reconstructed by a delaunay based algorithm and the grain volume is estimated by a numerical integration method. This proposed new method has been applied to two common types of grain storage, and experimental results shown this method is more effective and accurate, and it can also avoids the cumulative effect of errors when registering the overlapped area pair-wisely.