![]() ![]() Represents the relative direction from a pixel to the known keypoint. More specifically, if x k is one of the 2D keypoints projected from the 3D object, the network learns a conditional vector field v ( p | x k ) defined for each pixel p in a segmented region of the object where ![]() During the training stage, in contrast to methods that directly regress 2D keypoint locations from image segmentation of the model object, which have difficulty in predicting unseen keypoints of occluded and truncated objects, PV-net trains each pixel not only to learn the semantic label that associates it with a specific object but also unit vectors that represent the direction from this pixel to every known keypoint of the object. The robustness of PV-net stems from its vector field representation of object keypoints. It detects objects and estimates their orientations and translations using a two-stage pipeline: CNNs are first trained to predict the 2D keypoints of the object, as 2D keypoint detection is relatively easier than direct 3D localization from the whole image, and then the object pose is estimated from these keypoints through 2D–3D correspondences with an uncertainty-driven PnP algorithm. PV-net is a novel framework for model-based 6DoF object pose estimation. To resolve this issue, we move to RGB-only methods. The low quality of its depth output significantly undermines the results of registration-based methods. However, KG3’s native depth sensor is not capable of producing a reliable point cloud for objects that are 50 centimeters away. Given a decent point cloud of an object and a clear separation from the background, object pose estimation can be carried out using registration-based methods such as iterative closest point (ICP) algorithms and their variants. The main contributions are as follows:ģ.2. On the other hand, voting and regression methods by utilizing deep neural networks have a more robust performance.Ĭonsidering the robotic arm sensor performance and the system versatility, we input RGB images to train the pixel-wise voting network (PV-net), and utilized a depth camera to judge whether the detected object was real or not and assist grasping point detection. On one hand, correspondence methods such as scale invariant feature transform (SIFT) and spin image have a high dependence of depth sensor resolution, while template methods such as LineMod have high sensitivity to occlusion scenes. In addition, the algorithms of pose estimation can be divided into correspondence, template, voting and regression. In the detection part, there are three main tasks: object recognition, pose estimation and grasping point location. To improve the performance of the grasping system, the detection part has gained widespread attention. To date, it still is a challenge to grasp a texture-less irregular object placed in a heavy-occlusion scene. As a result, robotic grasping systems have shortcomings such as high computation cost, high latency and low accuracy. However, a major problem with detection occurs because strategies based on vision methods are sensitive to the actual working scene. Previous research employed machine learning methods to design descriptors, and showed good performance when the object was set in a simple environment or in simulation scenes with little interference and occlusion. The planning and controlling parts are well developed, while the detection parts have presented a challenge in recent years. In general, a robotic grasping system contains detection, planning and controlling parts. With the advantage of abundant information, strong robustness and low cost, computer vision techniques are increasingly being applied in intelligent systems. These kinds of systems can meet human demand in aspects such as packaging logistics, industrial production, medical services, etc. We expect a robotic system that can autonomously search the desired object, estimate its pose, grasp it, and move it to its target position. Today, intelligent systems play a vital role in human life with the rapid development of computer science. ![]()
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