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3-D Vision and Recognition

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Springer Handbook of Robotics

Abstract

In this chapter, we describe methods to be applied on a robot equipped with one or more camera sensors. Our goal is to present representations and models for both three-dimensional (3-D) motion and structure estimation as well as recognition. We do not delve into estimation and inference issues since these are extensively treated in other chapters. The same applies to the fusion with other sensors, which we heavily encourage but do not describe here.

In the first part we describe the main methods in 3-D inference from two-dimensional (2-D) images. We are at the point where we could propose a recipe, at least for a small spatial extent. If we are able to track a few visual features in our images, we are able to estimate the self-motion of the robot as well as its pose with respect to any known landmark. Having solutions for minimal case problems, the obvious way here is to apply random sample consensus. If no known 3-D landmark is given then the trajectory of the camera exhibits drift. From the trajectory of the camera, time windows over several frames are selected and a 3-D dense depth map is obtained through solving the stereo problem. Large-scale reconstructions based on camera only do raise challenges with respect to drift and loop closing.

In the second part we deal with recognition as appealed to robotics. The main challenge here is to detect an instance of an object and recognize or categorize it. Since in robotics applications an object of interest always resides in a cluttered environment any algorithm has to be insensitive to missing parts of the object of interest and outliers. The dominant paradigm is based on matching the appearance of pictures. Features are detected and quantized into visual words. Similarity is based on the difference between histograms of such visual words. Recognition has a long way to go but robotics provides the opportunity to explore an object and be active in the recognition process.

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Abbreviations

DOG:

difference of Gaussian

GPS:

global positioning system

HMM:

hidden Markov model

IMU:

inertial measurement units

MAP:

maximum a posteriori probability

MSER:

maximally stable extremal regions

RANSAC:

random sample consensus

RBC:

recognition-by-components

RGB:

red, green, blue

SIFT:

scale-invariant feature transformation

SLAM:

simultaneous localization and mapping

SVD:

singular value decomposition

SfM:

structure from motion

TF-IDF:

term-frequency inverse document frequency

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Authors and Affiliations

  1. Department of Computer and Information Science GRASP Laboratory, University of Pennsylvania, 3330 Walnut Street, 19104, Philadelphia, PA, USA

    Kostas Daniilidis Prof

  2. KTH Royal Institute of Technology, Teknikringen 14, 10044, Stockholm, Sweden

    Jan-Olof Eklundh Prof

Authors
  1. Kostas Daniilidis Prof
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  2. Jan-Olof Eklundh Prof
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Correspondence to Kostas Daniilidis Prof or Jan-Olof Eklundh Prof .

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  1. PRISMA Lab, Dipartimento di Informatica e Sistemistica, Universitá degli Studi di Napoli Federico II, 80125, Napoli, Italy, Via Claudio 21

    Bruno Siciliano Prof.

  2. Artificial Intelligence Laboratory, Department of Computer Science, Stanford University, 94305-9010, Stanford, CA, USA

    Oussama Khatib Prof.

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Daniilidis, K., Eklundh, JO. (2008). 3-D Vision and Recognition. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30301-5_24

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  • DOI: https://doi.org/10.1007/978-3-540-30301-5_24

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