In this paper we present a stereo visual odometry system for mobile robots that is not sensitive to uneven terrain. Two cameras is mounted perpendicular to the ground and height and traveled distance are calculated using normalized cross correlation. A method for evaluating the system is developed, where flower boxes containing representative surfaces are placed in a metal-working lathe. The cameras are mounted on the carriage which can be positioned manually with 0.1 mm accuracy. Images are captured every 10 mm over 700 mm. The tests are performed on eight different surfaces representing real world situations. The resulting error is less than 0.6% of traveled distance on surfaces where the maximum height variation is measured to 96 mm. The variance is measured for eight test runs, total 5.6 m, to 0.040 mm. This accuracy is sufficient for crop-scale agricultural operations.
In this thesis Stefan investigates how cameras can be used for localization of an agricultural mobile robot. He focuses on relative measurement that can be used to determine where a weeding tool is operating relative a weed detection sensor. It incorporates downward-facing perspective cameras, forward-facing perspective cameras and omnidirectional cameras. Stefan shows how the camera’s ego-motion can be estimated to obtain not only the position in 3D but also the orientation. He also shows how line structures in the field can be used to navigate a robot along the rows.