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The lack of spatial positional information of picking targets and low target localization accuracy are one of the key problems that limit the picking effect of strawberry picking robots. To address these problems, a target localization and segmentation model was firstly designed based on color information and convolutional neural network for strawberry image and target point cloud segmentation;secondly, an image-based strawberry pickability and obscuration recognition model was implemented;finally, a strawberry spatial localization and pose estimation model was designed and a strawberry picking point localization method was implemented. Based on this method, the estimation error of intact strawberry position was 4.03%, the estimation error of obscured strawberry position was 9.06%, and the comprehensive error of picking position was 2.3mm. In the actual picking experiment, the picking success rate was 92.6%, the average calculation time of each strawberry was about 92ms, and the average execution time of single strawberry picking action was about 5.7s. The experimental results can provide effective target localization information for strawberry picking robots, which can effectively meet the needs of actual picking scenarios.