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Semantic Segmentation with Rgbd Camera and Real-time 2D Mapping in Fields for Robot Mower
( Masahiro Moriya ),( Yutaka Kaizu ),( Kenji Imou ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1
Mowing is burdensome and automation by the robot mower is expected. For autonomous running of the robot mower, the accurate information of fields with various types of objects is necessary. In this research, we developed a real-time outdoor 2D mapping method based on semantic segmentation using RGBD camera for autonomous running of robot mower. In our method, first, pixel-wise classification image is obtained from RGB-D image using semantic segmentation, which is a technique of image processing by deep convolutional neural network (DCNN), and using the classification image and the depth image, the environmental information about the location and the type of objects in the surroundings is obtained. At the same time, the state (position, attitude, etc.) is estimated from output of GNSS receiver and Inertial Measurement Unit (IMU) using Extended Kalman Filter (EKF). By combining the environmental information and the state, 2D map is created in real-time. To verify our method, we used the differential driven four-wheel vehicle (with two driving wheels, two driven wheels) imitating an actual mower (with four driving wheels). The vehicle has an RGBD camera, a GNSS receiver, IMU and a control computer. As a result, the vehicle properly classified the surrounding objects and 2D map was created in real time. By using the map obtained by our method, autonomous running of the robot mower can be performed even in fields with various types of objects.