6-D Object Pose Estimation Based on Point Pair Matching for Robotic Grasp Detection.

The 6-D pose estimation is a critical work essential to achieve reliable robotic grasping. Currently, the prevalent method is reliant on keypoint correspondence. However, this approach hinges on the determination of object keypoint locations, alongside their detection and localization in real scenes. It also employs the random sample consensus (RANSAC)-based perspective-n-point (PnP) algorithm to solve the pose. Yet, it is nondifferentiable and incapable of backpropagation with loss during the training phase. Alternatively, the direct regression method, while speedy and differentiable, falls short in terms of pose estimation performance, and thus needs enhancement. In view of these gaps, we investigate PPM6D, a new method for 6-D object pose estimation based on regression and point pair matching. Our methodology begins with a proposed cross-fusion module, designed to achieve the fusion and complementation of RGB features and point cloud features. Subsequently, an attention module adjusts the features of the object's 3-D model. Finally, we design a point pair matching module for effective matching of points and characteristics, resulting in an integral matching and fusion. PPM6D is extensively trained and tested utilizing benchmark datasets like LINEMOD, occlusion LINEMOD (LINEMOD-occ), YCB-Video, and T-LESS dataset. Experimental results prove that PPM6D can outperform many keypoint-based pose estimation methods, given its relatively rapid speed, thereby offering novel regression-based pose estimation ideas. When applied to real-world scenarios of object pose estimation tasks and grasp tasks of an actual Baxter robot, PPM6D demonstrates superior performance as compared to most alternatives.