In this paper different algorithms for visual odometry are evaluated for navigating an agricultural weeding robot in outdoor field environment. Today there is an encoder wheel that keeps track of the weeding tools position relative the camera, but the system suffers from wheel slippage and errors caused by the uneven terrain. To overcome these difficulties the aim is to replace the encoders with visual odometry using the plant recognition camera. Four different optical flow algorithms are tested on four different surfaces, indoor carpet, outdoor asphalt, grass and soil. The tests are performed on an experimental platform. The result shows that the errors consist mainly of dropouts caused by overriding maximum speed, and of calibration error due to uneven ground. The number of dropouts can be reduced by limiting the maximum speed and detection of missing frames. The calibration problem can be solved using stereo cameras. This gives a height measurement and the calibration will be given by camera mounting. The algorithm using normalized cross-correlation shows the best result concerning number of dropouts, accuracy and calculation time.