By Topic

3-D perception and modeling

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Andreas Birk ; Jacobs University, Campus Ring 1, Bremen, Germany ; Narunas Vaskevicius ; Kaustubh Pathak ; Soeren Schwertfeger
more authors

In the context of the 2008 Lunar Robotics Challenge (LRC) of the European Space Agency (ESA), the Jacobs Robotics team investigated three-dimensional (3-D) perception and modeling as an important basis of autonomy in unstructured domains. Concretely, the efficient modeling of the terrain via a 3D laser range finder (LRF) is addressed. The underlying fast extraction of planar surface patches can be used to improve situational awareness of an operator or for path planning. 3D perception and modeling is an important basis for mobile robot operations in planetary exploration scenarios as it supports good situation awareness for motion level teleoperation as well as higher level intelligent autonomous functions. It is hence desirable to get long-range 3D data with high resolution, large field of view, and very fast update rates. 3D LRF have a high potential in this respect. In addition, 3D LRF can operate under conditions where standard vision based methods fail, e.g., under extreme light conditions. However, it is nontrivial to transmit the huge amount of data delivered by a 3D LRF to an operator station or to use this point cloud data as basis for higher level intelligent functions. Based on our participation in the LRC of the ESA, it is shown how the huge amount of 3D point cloud data from 3D LRF can be tremendously reduced. Concretely, large sets of points are replaced by planar surface patches that are fitted into the data in an optimal way. The underlying computations are very efficient and hence suited for online computations onboard of the robot.

Published in:

IEEE Robotics & Automation Magazine  (Volume:16 ,  Issue: 4 )