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Probabilistic Mapping for Unmanned Rotorcraft Using Point-Mass Targets and Quadtree Structures

[+] Author Affiliations
Derek S. Caveney, Yeonsik Kang, J. Karl Hedrick

University of California at Berkeley

Paper No. IMECE2005-82889, pp. 273-280; 8 pages
doi:10.1115/IMECE2005-82889
From:
  • ASME 2005 International Mechanical Engineering Congress and Exposition
  • Dynamic Systems and Control, Parts A and B
  • Orlando, Florida, USA, November 5 – 11, 2005
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 0-7918-4216-9 | eISBN: 0-7918-3769-6
  • Copyright © 2005 by ASME

abstract

In this paper, the authors present a technique for constructing probabilistic occupancy maps for unmanned rotorcraft. The mapping technique is Bayesian and assumes that a ranging sensor positioned on the rotorcraft is providing noisy target measurements in the presence of clutter. By running a multiple-model Kalman filter-based algorithm, all measurements are used to provide point-mass target state estimates and associated state covariances. Furthermore, the multiple-model algorithm provides probabilities that indicate whether each target is a true target or a false alarm. These three attributes of each target (the state, covariance, and true target probability) are used to build and update the occupancy map. The map is built upon a quadtree structure that allows for higher map resolution in occupied areas of the operating environment and quick access of obstacle locations. This aspect of a quadtree-structured map is particularly useful when transferring obstacle information to obstacle avoidance and route planning routines. The construction of occupancy maps resulting from this quadtree-based, probabilistic technique is demonstrated through simulations of a low-flying rotorcraft travelling through an urban landscape.

Copyright © 2005 by ASME

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