In this paper, we address the problem of learning 3D maps of the environment using a cheap sensor setup which consists of two standard web cams and a low cost inertial measurement unit. This setup is designed for lightweight or flying robots. Our technique uses visual features extracted from the web cams and estimates the 3D location of the landmarks via stereo vision. Feature correspondences are estimated using a variant of the PROSAC algorithm. Our mapping technique constructs a graph of spatial constraints and applies an efficient gradient descent-based optimization approach to estimate the most likely map of the environment. Our approach has been evaluated in comparably large outdoor and indoor environments. We furthermore present experiments in which our technique is applied to build a map with a blimp. ©2007 IEEE.

Learning maps in 3D using attitude and noisy vision sensors / Bastian, Steder; Grisetti, Giorgio; Slawomir, Grzonka; Cyrill, Stachniss; Axel, Rottmann; Wolfram, Burgard. - (2007), pp. 644-649. (Intervento presentato al convegno 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007 tenutosi a San Diego, CA nel 29 October 2007 through 2 November 2007) [10.1109/iros.2007.4399414].

Learning maps in 3D using attitude and noisy vision sensors

GRISETTI, GIORGIO;
2007

Abstract

In this paper, we address the problem of learning 3D maps of the environment using a cheap sensor setup which consists of two standard web cams and a low cost inertial measurement unit. This setup is designed for lightweight or flying robots. Our technique uses visual features extracted from the web cams and estimates the 3D location of the landmarks via stereo vision. Feature correspondences are estimated using a variant of the PROSAC algorithm. Our mapping technique constructs a graph of spatial constraints and applies an efficient gradient descent-based optimization approach to estimate the most likely map of the environment. Our approach has been evaluated in comparably large outdoor and indoor environments. We furthermore present experiments in which our technique is applied to build a map with a blimp. ©2007 IEEE.
2007
2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Learning maps in 3D using attitude and noisy vision sensors / Bastian, Steder; Grisetti, Giorgio; Slawomir, Grzonka; Cyrill, Stachniss; Axel, Rottmann; Wolfram, Burgard. - (2007), pp. 644-649. (Intervento presentato al convegno 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007 tenutosi a San Diego, CA nel 29 October 2007 through 2 November 2007) [10.1109/iros.2007.4399414].
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