The technology in current research scenario is marching towards automation forhigher productivity with accurate and precise product development. Vision andRobotics are domains which work to create autonomous systems and are the keytechnology in quest for mass productivity. The automation in an industry canbe achieved by detecting interactive objects and estimating the pose to manipulatethem. Therefore the object localization ( i.e., pose) includes position andorientation of object, has profound ?significance. The application of object poseestimation varies from industry automation to entertainment industry and fromhealth care to surveillance. The objective of pose estimation of objects is verysigni?cant in many cases, like in order for the robots to manipulate the objects,for accurate rendering of Augmented Reality (AR) among others.This thesis tries to solve the issue of object pose estimation using 3D dataof scene acquired from 3D sensors (e.g. Kinect, Orbec Astra Pro among others).The 3D data has an advantage of independence from object texture and invarianceto illumination. The proposal is divided into two phases : An o?ine phasewhere the 3D model template of the object ( for estimation of pose) is built usingIterative Closest Point (ICP) algorithm. And an online phase where the pose ofthe object is estimated by aligning the scene to the model using ICP, providedwith an initial alignment using 3D descriptors (like Fast Point Feature Transform(FPFH)).The approach we develop is to be integrated on two di?erent platforms :1)Humanoid robot `Pyrene' which has Orbec Astra Pro 3D sensor for data acquisition,and 2)Unmanned Aerial Vehicle (UAV) which has Intel Realsense Euclidon it. The datasets of objects (like electric drill, brick, a small cylinder, cake box)are acquired using Microsoft Kinect, Orbec Astra Pro and Intel RealSense Euclidsensors to test the performance of this technique. The objects which are used totest this approach are the ones which are used by robot. This technique is testedin two scenarios, fi?rstly, when the object is on the table and secondly when theobject is held in hand by a person. The range of objects from the sensor is 0.6to 1.6m. This technique could handle occlusions of the object by hand (when wehold the object), as ICP can work even if partial object is visible in the scene.
In pixels we trust: From Pixel Labeling to Object Localization and Scene Categorization
