The fruit industry would suffer a great shock, due mainly to the fact that its yield relies heavily on high labor inputs, but the rural population is aging with the ever-increasing development of cities in China. Autonomous production can bring an effective solution to such issues, further promoting the precision management in orchards. 3D light detection and ranging (LiDAR) sensor has made a much greater contribution to the autonomous navigation in the information acquisition for orchards, compared with the traditional 2D laser scanner. Specifically, LiDAR is a commonly-used remote sensing technique, where a laser is used to measure the distance to an illuminated target. In this study, an inter-row robot navigation was thus proposed in an orchard using 3D LiDAR. The complex three-dimensional scene was treated effectively, particularly with the dense canopy and trunks occluded by branches. A 3D LiDAR detection device was used to collect the environment information at first, and a pass-through filter was then used to correct the region of interest, where the noise was removed from the positioning task. Euclidean clustering was used to recognize the fruit trees around the robot, assuming that the tree branches were subjected to the normal distribution in the vertical direction. Body centers of trees were equivalent to the position of trees. Random sampling consensus and the least square were selected to fit the tree data using the parallelism between the tree rows. A complementary fusion was also put forward to combine two fittings. The centerline between tree rows was calculated and then treated as the target navigation line. In addition, a pure pursuit algorithm was refined using the differential chassis, considering the looking-forward distance and heading deviation. The validation experiments were carried out in a simulated hedgerow orchard and a real pear orchard. It was found that the tree rows successfully fitted with great ability to resist the interference from the environment in the first scenery. The heading positioning deviation was within 1.65°, and the lateral deviation was within 6.1 cm, when the robot walked along the centerline at a speed of 0.33 m/s. The tracking system automatically followed the centerline with a speed of 0.43 m/s, with an absolute lateral deviation of within 15cm. In the second scenery, the tracking system followed the centerline with two speeds of 0.68 and 1.35 m/s, where the absolute lateral deviations were not beyond 21.3 cm and 22.1 cm, respectively. The tracking system can be expected to serve as the automatic navigation with good robustness in standard orchards, including the hedgerow or complex three-dimensional orchard.