Trellis cultivation is a typical fruit tree planting, where a net-like shelf cover is formed on the top using a cross bar or lead wire with a support column to the bottom of a trellis, and the branches and vines are spread on the shelf. Grapes, pears, and kiwis are all suitable for planting in a trellis. In this study, taking a trellised vineyard as a research object, a fast online system of autonomous navigation was established to serve as one of the core technologies for orchard robots. The robots needed to drive autonomously and quickly when entering the rows of fruit trees or resuming operations on the way. It was therefore highly demanded to return independently the job line or online. Autonomous launching was widely utilized to evaluate the capabilities and performance of robot navigation. Nevertheless, the environment of the trellised orchard was seriously obstructing satellite signals. A natural shielding layer of satellite signals was also found under the dense tree canopy, as well as the arrangement of branches and vines in the trellised orchard. The shielding has made the navigation of satellite positioning unstable. An absolute satellite positioning was not suitable for the shed orchard. As such, the robots needed to autonomously perceive the actual environment, and then determine the subsequent pose. But there were most slender stems and sparsely planted stalks in the scaffolding in trellis structured orchard. Most autonomous navigation of agricultural machinery at present focused mainly on the local environmental characteristics of orchards. A great challenge still remained on the online performance of autonomous navigation, particularly on high quality and efficiency of operations. In this study, pose detection was proposed to realize the rapid launch of robots in the environment of scaffolding orchard using the relative positioning navigation, with emphasis on the fusion of electronic compass and LiAR heading. A priori scaffold orientation was input to the controller at the human-machine interface of the touch-sensitive serial screen, and then the electronic compass and LiDAR heading were combined to capture the precise pose of robots relative to the tree row, according to the dual indicators of pose deviation. The thresholds of body pose and state were classified to trigger the online trajectory program. Fast online was thus achieved with an optimal online angle. A self-developed grape robot was used as a test platform to carry out fast-on-line performance tests in a simulated trellised vineyard. The test results showed that the online time was 6.11, 7.15, 7.46, 7.74, and 8.9 s, respectively, while the online distance was 1.357, 1.367, 1.387, 1.383, and 1.403 m, respectively, under the constant speed of 0.3 m/s, the initial lateral deviation of 1.4m, and the initial heading deviation of -π/4, -π/18, 0, π/18, and π/4. The optimal online crawler robot was achieved for short online time and distance in the field-to-row online positioning of an orchard. Angular implementation was also to quickly go online. Consequently, the robot can pose and go online quickly and stably using the planned path under the conditions of large initial lateral and heading deviation. Compared with the traditional path tracking, the online performance of the autonomous navigation system was improved significantly for the scaffold orchard, including the less online time and shorter online distance. The finding can provide a potential reference to the unmanned operation in scaffold orchards.