Fernández, J.J.

Fornas, D.

Peñalvert, A.

Prats, M.

Sales, J.

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45Instrumentation Viewpoint / 15/ MARTECH 13PRIMITIVE SHAPE FITTING IN POINT CLOUDS FOR ENABLING AUTONOMOUS UNDERWATER GRASPINGM5D. Fornas116, A. Peñalvert117, M. Prats118, J. J. Fernández119, J. Sales120AbstractAutonomous grasping of unknown objects by a robot is a highly challenging skill that is receiving increasing attention in the last years, and is still more challenging in underwater environments, with highly unstructured scenarios, limited availabil-ity of sensors and adverse conditions that affect the robot perception and control systems. This paper describes an approach for autonomous grasping on underwa-ter primitive shaped objects from floating vehicles, in particular cylinder shaped objects like an amphora. Various sources of stereo information are used to gather 3D information in order to recognise the object and plan feasible grasp on it using a RANSAC primitive shape recognition algorithm.Keywords – autonomous grasping, underwater, point cloud, RANSAC, shape fitting.INTRODUCTIONExploration of the oceans is attracting the interest of many companies and in-stitutions. Remote Operated Vehicles (ROVs) are currently the most used ma-chines but the trend is to advance towards Autonomous Underwater Vehicles (AUVs). The approach holds lots of challenges, being one of the biggest the au-tonomous grasping and manipulation tasks. Within this context the most recent project focused on increasing autonomy is the project TRIDENT [1], where Prats presents in [2] a framework to grasp objects with limited user interaction which is further developed here. In this paper we present a method able to perform grasp tasks autonomously in the constrained, yet realistic, problem of grasping objects like an amphora. 3D RECONSTRUCTION AND SEGMENTATIONGrasping objects generally requires at least some partial 3D structure. In this case it can be gathered either using laser stripe reconstruction [2] or a stereo camera (real or virtual in the UWSim [3]). In the proposed experiments a ste-reo camera is attached to a vehicle and captures a pair of images from which a 3D reconstruction with respect to a fixed frame is performed. These images are processed with PCL [4] to continuously obtain a point cloud, which is then processed using downsampling and filtering algorithms.With this relevant point cloud a RANSAC algorithm, described in [5], is used twice to separate the object from the background as can be seen in Figure 1. First, the background plane is detected with a plane fitting algorithm. In the next step, other RANSAC algorithm is used to obtain the cylinder parameters associated to the real amphora. These algorithms are parameterized to enable fitting quality and performance control. The result of this sequence is a set of inliers that represent the detected amphora points, a point in the obtained cyl-inder model axis as well as its direction and cylinder radius.GRASP SPECIFICATIONUsing the cylinder model obtained a grasp is then computed. To avoid errors the grasp point is computed using the most significant points of the cylinder inliers, using the middle point of the cylinder axis as a starting grasp point. Then taking into account the amphora radius and desired approach distance and angle, the grasping end-effector frame is computed with respect to a world fixed frame, with this free variables allowing computing different grasp frames around the cylinder centre axis.After that, it is necessary to select a feasible grasp among the available ones. This can be done by computing the inverse kinematics of the whole arm kinematic chain and calculating its reachability. Our approach is to adopt a classical itera-tive inverse jacobian method. FUTURE WORK AND EXPERIMENTSThis approach will be tested in water tank conditions to perform a fully auton-omous grasp with a lightweight ARM5E arm, given the aforementioned con-straints of the problem.Here has been described a new framework that can be further developed to rec-ognise other primitive shapes such as spheres, cuboids or even more complex models that can be approximated with a set of primitive shapes, as shown in [6], in order to tackle the problem of completely unknown objects .ACKNOWLEDGMENTSThis research was partly supported by Spanish Ministry of Research and Innova-tion DPI2011-27977-C03 (TRITON Project), by the European Commission’s Sev-enth Framework Programme FP7/2007-2013 under Grant agreement 248497 (TRIDENT Project), by Foundation Caixa Castelló-Bancaixa PI.1B2011-17, and by Generalitat Valenciana ACOMP/2012/252.REFERENCES[1] M. Prats, J. C. García, J. J. Fernandez, R. Marín, and P. J. Sanz, “Advances in the specification and execution of underwater autonomous manipulation tasks”, In IEEE OCEANS 2011, Santander(Spain), June 2011.[2] M. Prats, J. J. Fernández, and P. J. Sanz, “Combining Template Tracking and Laser Peak Detection for 3D Reconstruction and Grasping in Underwater Envi-ronments”, In International Conference on Intelligent Robots and Systems (IROS), Algarve(Portugal), October 2012.[3] M. Prats, J. Pérez,  J. J. Fernandez, and P. J. Sanz, “An Open Source Tool for Simula-tion and Supervision of Underwater Intervention Missions”, In International Confer-ence on Intelligent Robots and Systems (IROS),  Algarve(Portugal), October 2012.[4] R. B. Rusu and S. Cousins, “3D is here: Point Cloud Library (PCL)”, in IEEE Interna-tional Conference on Robotics and Automation (ICRA), Shanghai (China), May 2011.[5] R. Schnabel, R. Wahl, and R. Klein, “Efficient RANSAC for Point-Cloud Shape De-tection”, In Computer Graphics Forum (Vol. 26, No. 2, pp. 214-226). Blackwell Pub-lishing Ltd., June 2007.[6] S. Garcia, Fitting primitive shapes to point clouds for robotic grasping, Doctoral dissertation, Master Thesis, Royal Institute of Technology, Sweden.Fig. 1 Object from background segmentation.Fig. 2 Grasp Specification shown in UWSim.

Primitive shape fitting in point clouds for enabling autonomous underwater grasping

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