To address the issue of low accuracy in picking pose establishment caused by overlapping fruit and challenging lighting conditions that introduced difficult-to-filter point cloud noise in orchard environments, an accurate method for establishing picking poses based on point cloud denoising using the random sample consensus (RANSAC) algorithm was proposed. Multiple potential spheres were detected from the pre-processed fruit point clouds by using the RANSAC algorithm. The sphere center with the shortest vertical distance to the point cloud capturing device was used to set a distance threshold, which facilitated further noise filtering from the target fruit point clouds and enhanced pose establishment accuracy. Subsequently, the denoised point clouds were spherefitted by using the least squares method to obtain the sphere center coordinates, which defined the precise picking position. Furthermore, by integrating the centroid coordinates from the corresponding binary mask image generated via an instance segmentation algorithm, an approach vector was constructed to determine the harvesting orientation, completing the pose establishment process. Experimental results on overlapping fruit point cloud denoising demonstrated that the proposed method effectively removed challenging point cloud noise from the target fruits. Pose establishment evaluations in an outdoor simulated orchard showed that the proposed method achieved a positioning accuracy of 15.0mm, enhancing the direct RANSAC fitting approach by up to 28.1% in accuracy and 76.0% in stability. Comparative harvesting trials in the orchard confirmed a successful positioning rate of 70.2% by using the proposed approach, which represented an increase of 23.4% over existing methods and a 38.4% improvement in harvesting success. The proposed method offered a robust solution for accurate fruit pose establishment in complex orchard environments.