Real-time stereo visual SLAM in large-scale environments based on SIFT fingerprints

  • Schleicher D
  • Bergasa L
  • Barea R
 et al. 
  • 55


    Mendeley users who have this article in their library.
  • 4


    Citations of this article.


This paper presents a new method for real-time SLAM calculation applied to autonomous robot navigation in large-scale environments without restrictions. It is exclusively based on the visual information provided by a cheap wide-angle stereo camera. Our approach divide the global map into local sub-maps identified by the so-called SIFT fingerprint. At the sub-map level (low level SLAM), 3D sequential mapping of natural land-marks and the robot location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. A high abstraction level to reduce the global accumulated drift, keeping real-time constraints, has been added (high level SLAM). This uses a correction method based on the SIFT fingerprints taking for each sub-map. A comparison of the low SLAM level using our method and SIFT features has been carried out. Some experimental results using a real large environment are presented.

Author-supplied keywords

  • Computer vision
  • Intelligent vehicles
  • SLAM

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Get full text


  • David Schleicher

  • Luis M. Bergasa

  • Rafael Barea

  • Elena López

  • Manuel Ocaña

  • Jesús Nuevo

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free