Trunk detection based on laser radar and vision data fusion

Tree trunks detection and their location information are needed to perform effective production and management in forestry and fruit farming. A novel algorithm based on data fusion with a vision camera and a 2D laser scanner was developed to detect tree trunks accurately. The transformation was built from a laser coordinate system to an image coordinate system, and the model of a rectangle calibration plate with two inward concave regions was established to implement data alignment between two sensors data. Then, data fusion and decision with Dempster-Shafer theory were achieved through integration of decision level after designing and determining basic probability assignments of regions of interesting (RoIs) for laser and vision data respectively. Tree trunk width was calculated by using laser data to determine basic probability assignments of RoIs of laser data. And a stripping segmentation algorithm was presented to determine basic probability assignments of RoIs of vision data, by calculating the matching level of RoIs like tree trunks. A robot platform was used to acquire data from sensors and to perform the developed tree trunk detection algorithm. Combined calibration tests were conducted to calculate a conversion matrix transforming from the laser coordinate system to the image coordinate system, and then field experiments were carried out in a real pear orchard under sunny and cloudy conditions, with trunk width measurement of 120 trees and 40 images processed by the presented stripping segmentation algorithm. Results showed the algorithm was successful to detect tree trunks and data fusion improved the ability for tree trunk detection. This algorithm could provide a new method for tree trunk detection and accurate production and management in orchards.