Abstract: Machine vision was one of the largest external environmental information sources, which was not only related to the capability of recognizing fruit fast and accurately, but also determined the reliability of harvesting robot directly. Designing and developing vision system customized and suitable to harvesting objects was of great significance to realize automatic fruit harvesting. CCD camera was the key component for existing vision systems, but it had a certain limitation for fruit recognition rate and position accuracy, because it was sensitive to unstructured harvesting environment, that was varying, unknown and open, especially to the problems such as the uncertainty of illumination conditions. Therefore, a laser vision system for apple harvesting robot was presented to avoid or reduce the effect of natural light. Based on the principle of time-of-flight, LMS211 was used for data acquisition with higher measurement precision and faster response speed. A linear motion unit was designed for assisting laser range finder to complete three dimensional scanning of object scene, which could adjust movement speed and travel of sliding table freely. A software based on Visual C++6.0 was developed for date collection and management, MATLAB would be used for range images generation, image preprocessing, fruit recognition and position in the later period. The different experimental results showed that scanning data could reflect the characteristics of fruit surface ideally within a certain measurement range (including valid scanning distance range from 200 mm to 1400mm, and ideal scanning angle range from 80° to 120° ) , and the suitable horizontal resolution was computed by the best estimator, which would increased imaging accuracy. Range image generated was easy to analyze geometrical features of fruits, leaves and branches, and hierarchical relationship between each other. At the same time, the kind of image was immune to various lighting condition, which background could be simplified by range constraint conveniently. All these advantages could provide even richer pattern information for fruit recognition.