Bastian Steder, Giorgio Grisetti, Slawomir Grzonka, Cyrill Stachniss, Axel Rottmann and Wolfram Burgard
Learning Maps in 3D using Attitude and Noisy Vision Sensors
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
using large-scale outdoor and indoor environments. We furthermore
present experiments in which our technique is applied to build a map
with a blimp.
Bibtex:
@InProceedings{steder07iros,
author = {Steder, S. and Grisetti, G. and Grzonka, S. and Stachniss, C. and Rottmann, A. and Burgard, W.},
title = {Learning Maps in 3D using Attitude and Noisy Vision Sensors},
booktitle ={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
ADDRESS = {San Diego, CA, USA},
year = 2007,
}
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