Currently, the target detection algorithm based on depth neural network is mostly used for pallet positioning and a rectangular box is generally utilized. The positioning accuracy of the pallet center point is not high enough, and the horizontal direction of the pallet cannot be estimated effectively. To solve this problem, this study proposes a pallet positioning method based on keypoint detection, which locates the pallet by detecting the four corners of the front outer outline. Firstly, due to the shortage of large-scale pallet data sets, the human posture estimation of CenterNet is introduced by transfer learning. Then the keypoint grouping method is improved, and the adaptive compensation is proposed for keypoint regression to improve the keypoint detection accuracy. According to the location of pallet keypoints, a method of pallet center point calculation and pallet horizontal direction estimation based on geometric constraints is proposed. Compared with the original CenterNet, the proposed method raises the positioning index AP^kp of pallet keypoint from 0.352 to 0.728, and the positioning accuracy ALP of pallet center point to 0.946. Meanwhile, it can effectively estimate the pallet horizontal direction and is of high practical value.