A Framework for Vision Based Bearing Only 3D SLAM

Patric Jensfelt, Danica Kragic , John Folkesson and Mårten Björkman

Abstract:

In the last few years, a number of simultaneous localization and mapping (SLAM) systems have been presented where visual sensors are used for feature extraction. The examples include 2D and 3D maps where either a single, stereo or a multi-camera system was used. In many real applications, a single camera system is the most appealing one due to the low cost for the hardware. However, single camera SLAM suffers from the problem that features' depth cannot be determined from a single image. This paper presents a framework for 3D vision based bearing only SLAM using a camera. The focus is on the management of the features so that we can achieve real-time performance in extraction, matching and loop detection. For matching image features to map landmarks a modified, rotationally variant SIFT descriptor is used which is more appropriate to the situation of a robot mounted camera. We also use a different feature extractor than originally proposed for SIFT which is more precisely localized in the image than the more diffuse Difference-of-Gaussians features. The richness of image data is beneficial to matching of features between frames but also leads to a large number of poorly localized features. The goal here is to have both robust matching from many vision features and a few high quality 3D localized features for use by the SLAM algorithm. The framework has been combined with an EKF implementation for SLAM. Experiments performed in indoor environments are presented. These experiments demonstrate the validity and effectiveness of the approach. In particular they show how the robot is able to successfully match current image features to the map when revisiting an area.

BibTeX Entry:

@InProceedings{Jensfelt06a,
  author =       {Patric Jensfelt and Danica Kragic and John Folkesson and M{\aa}rten Bj{\"o}rkman},
  title =        {A Framework for Vision Based Bearing Only {3D} {SLAM}},
  booktitle =    {Proc.~of the IEEE International Conference on Robotics and Automation (ICRA'06)},
  year =         2006,
  address =      {Orlando, FL},
  month =        may
}

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