Multi-feature fusion tree trunk detection and orchard mobile robot localization using camera/ultrasonic sensors

Abstract

In a semi-structured orange orchard, multi-tree trunk detection is an effective method for mobile robot localization. However, because of the complex background of the natural orchard environment, dwarf orange tree trunks are easy to misrecognize. In this paper, we present a novel tree trunk detection method based onmultiplecameras and ultrasonicsensors integration technology. These devices are integrated intoa single organicmechanical structure that can rotate to detect the surrounding orchard environment such that they can reduce the non-detectionzone. Multi-feature fusion will be used in this study. First, histograms of oriented gradient (HOG) and support vector machine (SVM) are used to train an initial tree trunk classifier. Next, the gray scale histogram features of the tree trunk and non-trunk images are extracted to optimize the classifier. Finally, the Roberts cross edge detector is used to extract the trunk’s gradient histogram features, which will improve the recognition accuracy of the classifier. The orange tree trunk recognition experiments exhibited a recall rate and accuracy of 92.14% and 95.49%, respectively. On this basis, ultrasonic sensors are used to get the location data of the trunks and a moving average filter is used to reduce the error of mobile robot localization. The experiment showed that the average localization error was approximately 62 mm (2.5%), and the robot moved stably and precisely along the road of the semi-structured orange orchard.