Systems and methods for detecting grasp poses for handling target objects

Abstract

Systems and methods for detecting grasping poses for handling target objects is disclosed. The system solves problem of grasp pose detection and finding suitable graspable affordance for picking objects from a confined and cluttered space, such as the bins of a rack in a retail warehouse by creating multiple surface segments within bounding box obtained from a neural network based object recognition module. Surface patches are created using a region growing technique in depth space based on surface normals directions. A Gaussian Mixture Model based on color and depth curvature is used to segment surfaces belonging to target object from background, thereby overcoming inaccuracy of object recognition module trained on a smaller dataset resulting in larger bounding boxes for target objects. Target object shape is identified by using empirical rules on surface attributes thereby detecting graspable affordances and poses thus avoiding collision with neighboring objects and grasping objects more successfully.