A visual odometer was developed for an autonomous greenhouse sprayer to estimate vehicle
translation and rotation relative to the world coordinate system during navigation. Digital images
were taken from a CCD camera mounted on the robot. 7 x 7 pixel features were selected in the
image using the KLT algorithm (Csetverikov, 2004). Features were tracked from image to image
by finding the best 7 x 7 pixel match of the feature within a 25 x 25 pixel search box. By
analyzing the movement of these features, vehicle rotation and translation were estimated. Five
features were tracked with the odometer. Tests were run to verify the visual odometer’s accuracy
during translation, rotation, and on various surfaces. The visual odometer ran at an average of 10
Hz during experimentation. Translation tests of the odometer in a lab environment gave an
average error of 4.85 cm for a 30.5 cm forward translation and 12.4 cm average error for a 305
cm translation. Rotation tests of the odometer in a lab environment gave an average error of 1°
for a 45° rotation and an 8° error for a 180° rotation about the vehicle z-axis. Tests completed on
concrete, sand, and gravel demonstrated adaptability of the odometer on different ground
surfaces that are common in greenhouses. The visual odometer was successfully integrated into a
visual navigation system for intersection navigation of an autonomous greenhouse sprayer.