A 3D shape segmentation approach for robot grasping by parts

Neuro-psychological findings have shown that human perception of objects is based on part decomposition. Most objects are made of multiple parts which are likely to be the entities actually involved in grasp affordances. Therefore, automatic object recognition and robot grasping should take advantage from 3D shape segmentation. This paper presents an approach toward planning robot grasps across similar objects by part correspondence. The novelty of the method lies in the topological decomposition of objects that enables high-level semantic grasp planning.In particular, given a 3D model of an object, the representation is initially segmented by computing its Reeb graph. Then, automatic object recognition and part annotation are performed by applying a shape retrieval algorithm. After the recognition phase, queries are accepted for planning grasps on individual parts of the object. Finally, a robot grasp planner is invoked for finding stable grasps on the selected part of the object. Grasps are evaluated according to a widely used quality measure. Experiments performed in a simulated environment on a reasonably large dataset show the potential of topological segmentation to highlight candidate parts suitable for grasping.     

Object interaction and task programming by demonstration in visuo-haptic augmented reality

A visuo-haptic augmented reality system is presented for object manipulation and task learning from human demonstration. The proposed system consists of a desktop augmented reality setup where users operate a haptic device for object interaction. Users of the haptic device are not co-located with the environment where real objects are present. A three degrees of freedom haptic device, providing force feedback, is adopted for object interaction by pushing, selection, translation and rotation. The system also supports physics-based animation of rigid bodies. Virtual objects are simulated in a physically plausible manner and seem to coexist with real objects in the augmented reality space. Algorithms for calibration, object recognition, registration and haptic rendering have been developed. Automatic model-based object recognition and registration are performed from 3D range data acquired by a moving laser scanner mounted on a robot arm. Several experiments have been performed to evaluate the augmented reality system in both single-user and collaborative tasks. Moreover, the potential of the system for programming robot manipulation tasks by demonstration is investigated. Experiments show that a precedence graph, encoding the sequential structure of the task, can be successfully extracted from multiple user demonstrations and that the learned task can be executed by a robot system.     

A 3D GIS-based interactive registration mechanism for outdoor augmented reality system

Registering virtual objects with reference to their corresponding real-world objects plays a key role in augmented reality (AR) system. Although there have been a lot of work on using vision-based method to perform registration for indoor AR system, it is very difficult to apply such registration method for outdoor AR system due to the inability to modify the objects in outdoor environment and the huge range of working area. 3D Geographic Information System (GIS) is capable of providing an outdoor virtual geographic environment where users are located at, which may provide users with a corresponding virtual object for the one in the physical world. In this study, a 3D GIS-based registration mechanism is proposed for outdoor AR system. Specifically, an easy-use interactive method for precise registration was developed to improve the performance of the registration. To implement the registration mechanism, an outdoor AR system built upon 3D GIS was developed, named Augmented Reality Geographical Information System (ARGIS). ARGIS has the capability of performing precise registration in outdoor environment without using traditional vision tracking method, which thus enables users to arbitrarily manipulate the system. A prototype was developed to conduct experiment on the campus of Peking University, Beijing, China to test the proposed registration mechanism. The experiment shows that the developed registration mechanism is feasible and efficient in the outdoor environment. The ARGIS is expected to enrich the applications of outdoor AR system, including but not limited to underground facility mapping, emergency rescue and urban planning.     

3D Object Recognition in Cluttered Environments by Segment-Based Stereo Vision

We propose a new method for 3D object recognition which uses segment-based stereo vision. An object is identified in a cluttered environment and its position and orientation (6 dof) are determined accurately enabling a robot to pick up the object and manipulate it. The object can be of any shape (planar figures, polyhedra, free-form objects) and partially occluded by other objects. Segment-based stereo vision is employed for 3D sensing. Both CAD-based and sensor-based object modeling subsystems are available. Matching is performed by calculating candidates for the object position and orientation using local features, verifying each candidate, and improving the accuracy of the position and orientation by an iteration method. Several experimental results are presented to demonstrate the usefulness of the proposed method.     

Shape Reconstruction Incorporating Multiple Nonlinear Geometric Constraints

This paper deals with the reconstruction of three-dimensional (3D) geometric shapes based on observed noisy 3D measurements and multiple coupled nonlinear shape constraints. Here a shape could be a complete object, a portion of an object, a part of a building etc. The paper suggests a general incremental framework whereby constraints can be added and integrated in the model reconstruction process, resulting in an optimal trade-off between minimization of the shape fitting error and the constraint tolerances. After defining sets of main constraints for objects containing planar and quadric surfaces, the paper shows that our scheme is well behaved and the approach is valid through application on different real parts. This work is the first to give such a large framework for the integration of numerical geometric relationships in object modeling from range data. The technique is expected to have a great impact in reverse engineering applications and manufactured object modeling where the majority of parts are designed with intended feature relationships.     

Registering multiview range data to create 3D computer objects

Concerns the problem of range image registration for the purpose of building surface models of 3D objects. The registration task involves finding the translation and rotation parameters which properly align overlapping views of the object so as to reconstruct from these partial surfaces, an integrated surface representation of the object. The registration task is expressed as an optimization problem. We define a function which measures the quality of the alignment between the partial surfaces contained in two range images as produced by a set of motion parameters. This function computes a sum of Euclidean distances from control points on one surfaces to corresponding points on the other. The strength of this approach is in the method used to determine point correspondences. It reverses the rangefinder calibration process, resulting in equations which can be used to directly compute the location of a point in a range image corresponding to an arbitrary point in 3D space. A stochastic optimization technique, very fast simulated reannealing (VFSR), is used to minimize the cost function. Dual-view registration experiments yielded excellent results in very reasonable time. A multiview registration experiment took a long time. A complete surface model was then constructed from the integration of multiple partial views. The effectiveness with which registration of range images can be accomplished makes this method attractive for many practical applications where surface models of 3D objects must be constructed     

RGBD融合明暗恢复形状的全视角三维重建技术研究

为了高效、高精度、低成本地实现对物体的全视角三维重建, 提出一种使用深度相机融合光照约束实现全视角三维重建的方法。该重建方法中,在进行单帧重建时采用RGBD深度图像融合明暗恢复形状(Shape from shading,SFS)的重建方法, 即在原有的深度数据上加上额外的光照约束来优化深度值; 在相邻两帧配准时, 采用快速点特征直方图（Fast point feature histograms, FPFH）特征进行匹配并通过随机采样一致性（Random sample consensus, RANSAC）滤除错误的匹配点对求解粗配准矩阵, 然后通过迭代最近点（Iterative closest point, ICP）算法进行精配准得出两帧间的配准矩阵; 在进行全视角的三维重建时, 采用光束平差法优化相机位姿, 从而消除累积误差使首尾帧完全重合, 最后融合生成一个完整的模型。该方法融入了物体表面的光照信息,因此生成的三维模型更为光顺,也包含了更多物体表面的细节信息,提高了重建精度;同时该方法仅通过单张照片就能在自然光环境下完成对多反射率三维物体的重建,适用范围更广。本文方法的整个实验过程通过手持深度相机就能完成,不需要借助转台,操作更加方便。     

Grasp programming by demonstration in virtual reality with automatic environment reconstruction

A virtual reality system enabling high-level programming of robot grasps is described. The system is designed to support programming by demonstration (PbD), an approach aimed at simplifying robot programming and empowering even unexperienced users with the ability to easily transfer knowledge to a robotic system. Programming robot grasps from human demonstrations requires an analysis phase, comprising learning and classification of human grasps, as well as a synthesis phase, where an appropriate human-demonstrated grasp is imitated and adapted to a specific robotic device and object to be grasped. The virtual reality system described in this paper supports both phases, thereby enabling end-to-end imitation-based programming of robot grasps. Moreover, as in the PbD approach robot environment interactions are no longer explicitly programmed, the system includes a method for automatic environment reconstruction that relieves the designer from manually editing the pose of the objects in the scene and enables intelligent manipulation. A workspace modeling technique based on monocular vision and computation of edge-face graphs is proposed. The modeling algorithm works in real time and supports registration of multiple views. Object recognition and workspace reconstruction features, along with grasp analysis and synthesis, have been tested in simulated tasks involving 3D user interaction and programming of assembly operations. Experiments reported in the paper assess the capabilities of the three main components of the system: the grasp recognizer, the vision-based environment modeling system, and the grasp synthesizer.     

Model-based 3D object detection

Fast detection of objects in a home or office environment is relevant for robotic service and assistance applications. In this work we present the automatic localization of a wide variety of differently shaped objects scanned with a laser range sensor from one view in a cluttered setting. The daily-life objects are modeled using approximated Superquadrics, which can be obtained from showing the object or another modeling process. Detection is based on a hierarchical RANSAC search to obtain fast detection results and the voting of sorted quality-of-fit criteria. The probabilistic search starts from low resolution and refines hypotheses at increasingly higher resolution levels. Criteria for object shape and the relationship of object parts together with a ranking procedure and a ranked voting process result in a combined ranking of hypothesis using a minimum number of parameters. The experimental evaluation of the method and experiments from cluttered table top scenes demonstrate the effectiveness and robustness of the approach, feasible for real world object localization and robot grasp planning.     

基于CV／CAD的三维物体几何建模

在虚拟现实等技术领域中,都涉及到由现实世界中的实际景物建立对应的计算机描述的虚拟景物的问题,为此提出了利用计算机视觉与CAD几何建模技术相结合的三维珠体建模途径,首先通过编码光栅方法获取三维物体的深度图象,并采用数学形态学的方法加以分割,然后利用代数曲面拟合手段对分割后的三维曲面片进行重建,并使用CAD几何建模工具由重建的曲面片构成物体的几何模型,该文给出了初步的实验结果,证明所提出的技术途径基本可行。     

A strategy for fast grasping of unknown objects using partial shape information from range sensors

In this paper, we present a strategy for fast grasping of unknown objects based on the partial shape information from range sensors for a mobile robot with a parallel-jaw gripper. The proposed method can realize fast grasping of an unknown object without needing complete information of the object or learning from grasping experience. Information regarding the shape of the object is acquired by a 2D range sensor installed on the robot at an inclined angle to the ground. Features for determining the maximal contact area are extracted directly from the partial shape information of the unknown object to determine the candidate grasping points. Note that since the shape and mass are unknown before grasping, a successful and stable grasp cannot be in fact guaranteed. Thus, after performing a grasping trial, the mobile robot uses the 2D range sensor to judge whether the object can be lifted. If a grasping trial fails, the mobile robot will quickly find other candidate grasping points for another trial until a successful and stable grasp is realized. The proposed approach has been tested in experiments, which found that a mobile robot with a parallel-jaw gripper can successfully grasp a wide variety of objects using the proposed algorithm. The results illustrate the validity of the proposed algorithm in term of the grasping time.     

Towards fully automatic reliable 3D acquisition: From designing imaging network to a complete and accurate point cloud

This paper describes a novel system for accurate 3D digitization of complex objects. Its main novelties can be seen in the new approach, which brings together different systems and tools in a unique platform capable of automatically generating an accurate and complete model for an object of interest. This is performed through generating an approximate model of the object, designing a stereo imaging network for the object with this model and capturing the images at the designed postures through exploiting an inverse kinematics method for a non-standard six degree of freedom robot. The images are then used for accurate and dense 3D reconstruction using photogrammetric multi-view stereo method in two modes, including resolving scale with baseline and with control points. The results confirm the feasibility of using Particle Swarm Optimization in solving inverse kinematics for this non-standard robot. The system provides this opportunity to test the effect of incidence angle on imaging network design and shows that the matching algorithms work effectively for incidence angle of 10°. The accuracy of the final point cloud generated with the system was tested in two modes through a comparison with a dataset generated with a close range 3D colour laser scanner.     

3D Free-Form Object Recognition Using Indexing by Contour Features

We address the problem of recognizing free-form 3D objects from a single 2D intensity image. A model-based solution within the alignment paradigm is presented which involves three major schemes—modeling, matching, and indexing. The modeling scheme constructs a set of model aspects which can predict the object contour as seen from any viewpoint. The matching scheme aligns the edgemap of a candidate model to the observed edgemap using an initial approximate pose. The major contribution of this paper involves the indexing scheme and its integration with modeling and matching to perform recognition. Indexing generates hypotheses specifying both candidate model aspects and approximate pose and scale. Hypotheses are ordered by likelihood based on prior knowledge of pre-stored models and the visual evidence from the observed objects. A prototype implementation has been tested in recognition and localization experiments with a database containing 658 model aspects from twenty 3D objects and eighty 2D objects. Bench tests and simulations show that many kinds of objects can be handled accurately and efficiently even in cluttered scenes. We conclude that the proposed recognition-by-alignment paradigm is a viable approach to many 3D object recognition problems.     

Textures revisited

We describe texture generation methods for complex objects. Recently developed 3D scanning devices and high-resolution cameras can capture the complex geometry of an object and yield high-resolution images. However, generating a textured model from this input data is still a difficult problem. This task is divided into three subproblems: parameterization, texture combination, and texture restoration. A low-distortion parameterization method is presented, which minimizes geometry stretch energy. Photographs of the object taken from multiple viewpoints under modestly uncontrolled illumination conditions are merged into a seamless texture using our new texture combination method. We also demonstrate a texture restoration method that can fill in missing pixel information when the input photographs do not provide sufficient information to cover the entire surface due to self-occlusion or registration errors. Our methods are fully automatic, except for the registration process between a 3D model and input photographs. We demonstrate the application of our method to human face models for evaluation. The techniques presented in this paper make a consistent and complete pipeline to generate the texture of a complex object.     

Augmented reality-based robot teleoperation system using RGB-D imaging and attitude teaching device

Augmented reality (AR)-based programming using the demonstration method has been widely studied. However, studies on AR-based programming for remote robots are lacking because of the limitation of human–computer interaction. This paper proposes an AR-based robot teleoperation system and method using RGB-D imaging and an attitude teaching device. By sending the color and depth images of the remote robot environment to the local side, the operators can complete the teleoperation of the robot at the local side. First, the operators select key positions on the motion path of the robot endpoint from color images via a mouse, and the computer calculates the 3D coordinates of these key points in the robot base coordinate system to complete the position teaching process. In the robot attitude teaching process, the AR technology is used to superimpose the virtual robot model onto the color images of the robot teleoperation environment, so as to make the virtual robot endpoint to move along the teaching path. An operator can use the portable attitude teaching device designed in this study to control the robot movement parameters, such as the attitude and motion speed, during the movement of the virtual robot. After the position and attitude teaching processes, the robot movement trajectory can be generated. To make the base coordinate system of the virtual model consistent with that of the physical robot, we propose an online AR registration method, which does not require manually placing the AR registration marker. The proposed AR-based robot teleoperation system can quickly and easily complete robot teleoperation at the local side.     

协作环境下的人机距离建模与最小距离迭代算法

为了使机器人适应更广泛、更复杂的任务需求,实现人与机器人的协作与共融,精确并实时地计算人与机器人之间的相对距离成为了不可避免的问题.针对该问题,提出了一种协作环境下的人机距离建模方法以及计算人机间最小距离的迭代算法.首先,利用机器人的3D模型构建机器人结构特征,并通过3D视觉传感器提取人体骨骼特征,将以上2组特征映射到同一坐标空间中建立协作环境下的人机距离模型.然后,在此模型的基础上迭代计算人与机器人间的最小距离并给出对应的空间位置点.最后以ABB公司的YuMi机器人为测试对象进行人机最小距离测量实验,实验结果表明该方法降低了建模难度、实现了计算的实时性,验证了该建模方法与迭代算法的有效性和实用性.     

Object Completion using k‐Sparse Optimization

We present a new method for the completion of partial globally‐symmetric 3D objects, based on the detection of partial and approximate symmetries in the incomplete input dataset. In our approach, symmetry detection is formulated as a constrained sparsity maximization problem, which is solved efficiently using a robust RANSAC‐based optimizer. The detected partial symmetries are then reused iteratively, in order to complete the missing parts of the object. A global error relaxation method minimizes the accumulated alignment errors and a non‐rigid registration approach applies local deformations in order to properly handle approximate symmetry. Unlike previous approaches, our method does not rely on the computation of features, it uniformly handles translational, rotational and reflectional symmetries and can provide plausible object completion results, even on challenging cases, where more than half of the target object is missing. We demonstrate our algorithm in the completion of 3D scans with varying levels of partiality and we show the applicability of our approach in the repair and completion of heavily eroded or incomplete cultural heritage objects.     

Interactive Image‐Guided Modeling of Extruded Shapes

A recent trend in interactive modeling of 3D shapes from a single image is designing minimal interfaces, and accompanying algorithms, for modeling a specific class of objects. Expanding upon the range of shapes that existing minimal interfaces can model, we present an interactive image‐guided tool for modeling shapes made up of extruded parts. An extruded part is represented by extruding a closed planar curve, called base, in the direction orthogonal to the base. To model each extruded part, the user only needs to sketch the projected base shape in the image. The main technical contribution is a novel optimization‐based approach for recovering the 3D normal of the base of an extruded object by exploring both geometric regularity of the sketched curve and image contents. We developed a convenient interface for modeling multi‐part shapes and a method for optimizing the relative placement of the parts. Our tool is validated using synthetic data and tested on real‐world images.     

A flexible similarity measure for 3D shapes recognition

This paper is devoted to presenting a new strategy for 3D objects recognition using a flexible similarity measure based on the recent modeling wave (MW) topology in spherical models. MW topology allows us to establish an n-connectivity relationship in 3D objects modeling meshes. Using the complete object model, a study on considering different partial information of the model has been carried out to recognize an object. For this, we have introduced a new feature called cone-curvature (CC), which originates from the MW concept. CC gives an extended geometrical surroundings knowledge for every node of the mesh model and allows us to define a robust and adaptable similarity measure between objects for a specific model database. The defined similarity metric has been successfully tested in our lab using range data of a wide variety of 3D shapes. Finally, we show the applicability of our method presenting experimentation for recognition on noise and occlusion conditions in complex scenes.