Learn More
Feature matching is at the base of many computer vi­ sion problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for de­ tection and matching. In this paper, we propose a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise. We demonstrate through(More)
— Many popular problems in robotics and computer vision including various types of simultaneous localization and mapping (SLAM) or bundle adjustment (BA) can be phrased as least squares optimization of an error function that can be represented by a graph. This paper describes the general structure of such problems and presents g 2 o, an open-source C++(More)
Robotic systems are becoming smaller, lower power, and cheaper, enabling their application in areas not previously considered. This is true of vision systems as well. SRI's Small Vision Module (SVM) is a compact, inexpensive realtime device for computing dense stereo range images, which are a fundamental measurement supporting a wide range of computer(More)
—Many successful indoor mapping techniques employ frame-to-frame matching of laser scans to produce detailed local maps, as well as closing large loops. In this paper, we propose a framework for applying the same techniques to visual imagery. We match visual frames with large numbers of point features, using classic bundle adjustment techniques from(More)
We present a method for detecting 3D objects using multi-modalities. While it is generic, we demonstrate it on the combination of an image and a dense depth map which give complementary object information. It works in real-time, under heavy clutter, does not require a time consuming training stage, and can handle untextured objects. It is based on an(More)
— In this paper we address the topic of feature extraction in 3D point cloud data for object recognition and pose identification. We present a novel interest keypoint extraction method that operates on range images generated from arbitrary 3D point clouds, which explicitly considers the borders of the objects identified by transitions from foreground to(More)