Showing papers by "Gary Bradski published in 2010"

Fast 3D recognition and pose using the Viewpoint Feature Histogram

Abstract: We present the Viewpoint Feature Histogram (VFH), a descriptor for 3D point cloud data that encodes geometry and viewpoint. We demonstrate experimentally on a set of 60 objects captured with stereo cameras that VFH can be used as a distinctive signature, allowing simultaneous recognition of the object and its pose. The pose is accurate enough for robot manipulation, and the computational cost is low enough for real time operation. VFH was designed to be robust to large surface noise and missing depth information in order to work reliably on stereo data.

Autonomous door opening and plugging in with a personal robot

Abstract: We describe an autonomous robotic system capable of navigating through an office environment, opening doors along the way, and plugging itself into electrical outlets to recharge as needed. We demonstrate through extensive experimentation that our robot executes these tasks reliably, without requiring any modification to the environment. We present robust detection algorithms for doors, door handles, and electrical plugs and sockets, combining vision and laser sensors. We show how to overcome the unavoidable shortcoming of perception by integrating compliant control into manipulation motions. We present a visual-differencing approach to high-precision plug-insertion that avoids the need for high-precision hand-eye calibration.

Depth-encoded hough voting for joint object detection and shape recovery

Abstract: Detecting objects, estimating their pose and recovering 3D shape information are critical problems in many vision and robotics applications This paper addresses the above needs by proposing a new method called DEHV - Depth-Encoded Hough Voting detection scheme Inspired by the Hough voting scheme introduced in [13], DEHV incorporates depth information into the process of learning distributions of image features (patches) representing an object category DEHV takes advantage of the interplay between the scale of each object patch in the image and its distance (depth) from the corresponding physical patch attached to the 3D object DEHV jointly detects objects, infers their categories, estimates their pose, and infers/decodes objects depth maps from either a single image (when no depth maps are available in testing) or a single image augmented with depth map (when this is available in testing) Extensive quantitative and qualitative experimental analysis on existing datasets [6,9,22] and a newly proposed 3D table-top object category dataset shows that our DEHV scheme obtains competitive detection and pose estimation results as well as convincing 3D shape reconstruction from just one single uncalibrated image Finally, we demonstrate that our technique can be successfully employed as a key building block in two application scenarios (highly accurate 6 degrees of freedom (6 DOF) pose estimation and 3D object modeling)