Reconstruction of 3D Natural Field Scenes Based on SLAM Robot

Mobile robots have already been widely applied in agriculture fields. In order to perform complex tasks such as localization, mapping and path planning, mobile robots need to understand their environment. Therefore, the perception ability is important to robots. In the past twenty years, Simultaneous Localization and Mapping has been a popular algorithm to solve localization and mapping problems. In agricultural applications, robots need to overcome bumpy surfaces and should avoid inducing destruction to plant growths. Hence, the assumptions of robot poses need to be represented by six degree of freedom, not just flat surface movements. In order to estimate the 6D poses, 3D information was acquired by our custom-made of 3D laser scanner. The ICP algorithm then merged different scenes together to reconstruct the whole environment. However, errors are accumulated in the ICP algorithm, so a global optimization algorithm was applied to deal with this accumulated error. We had successfully reconstructed two large scale environments that contain millions of points. The method we proposed here has a RMSE of 55.94 centimeters. It is believed that this system has different applications such as agriculture field investigation, virtual reality and geographic information system.