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Efficient viewpoint assignment for urban texture documentation

Published:04 November 2009Publication History

ABSTRACT

We envision participatory texture documentation (PTD) as a process in which a group of users (dedicated individuals and/or general public) with camera-equipped mobile phones participate in collaborative collection of urban texture information. PTD enables inexpensive, scalable and high resolution urban texture documentation. We have proposed to implement PTD in two steps [10]. At the first step, termed viewpoint selection, a minimum number of points in the urban environment are selected from which the texture of the entire urban environment (the part visible to cameras) can be collected/captured. At the second step, called viewpoint assignment, the selected viewpoints are assigned to the participating users such that given a limited number of users with various constraints (e.g., restricted available time) users can collectively capture the maximum amount of texture information within a limited time interval. In this paper, we focus on the viewpoint assignment problem. We first prove that this problem is an NP-hard problem, and therefore, the optimal solution for viewpoint assignment fails to scale as the extent of the urban environment and the number of participating users grow. Subsequently, we propose a family of heuristics for efficient viewpoint assignment to reduce the assignment running time while ensuring an almost complete texture collection. We study, profile and verify our proposed solutions comparatively by both rigorous analysis and extensive experiments.

References

  1. A. Blum, S. Chawla, D. R. Karger, T. Lane, A. Meyerson, and M. Minkoff. Approximation algorithms for orienteering and discounted-reward tsp. In FOCS, pages 46--55, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. P. H. Borgstrom, A. Singh, B. L. Jordan, G. S. Sukhatme, M. A. Batalin, and W. J. Kaiser. Energy based path planning for a novel cabled robotic system. In IROS, pages 1745--1751, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  3. I. Chao, B. L. Golden, and E. A. Wasil. The team orienteering problem. European Journal of Operational Research, 88(3):464--474, 1996.Google ScholarGoogle ScholarCross RefCross Ref
  4. I.-M. Chao, B. L. Golden, and E. A. Wasil. A fast and effective heuristic for the orienteering problem. European Journal of Operational Research, 88(3):475--489, 1996.Google ScholarGoogle ScholarCross RefCross Ref
  5. C. Chekuri, N. Korula, and M. Pál. Improved algorithms for orienteering and related problems. In SODA 2008, pages 661--670, Philadelphia, PA, USA, 2008. Society for Industrial and Applied Mathematics. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. C. Chekuri and M. Pal. A recursive greedy algorithm for walks in directed graphs. In FOCS 2005, pages 245--253, Washington, DC, USA, 2005. IEEE Computer Society. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. K. Chen and S. Har-Peled. The euclidean orienteering problem revisited. SIAM J. Comput., 38(1):385--397, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. M. Fischetti, J. J. S. Gonzalez, and P. Toth. Solving the orienteering problem through branch-and-cut. INFORMS J. on Computing, 10(2):133--148, 1998.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. R. J. Fowler and J. J. Little. Automatic extraction of irregular network digital terrain models. In SIGGRAPH '79: Proceedings of the 6th annual conference on Computer graphics and interactive techniques, pages 199--207, New York, NY, USA, 1979. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. H. Shirani-Mehr, F. Banaei-Kashani, and C. Shahabi. Efficient viewpoint selection for urban texture documentation. In Third International Conference on Geosensor Networks, July 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. A. Singh, A. Krause, C. Guestrin, W. J. Kaiser, and M. A. Batalin. Efficient planning of informative paths for multiple robots. In IJCAI, pages 2204--2211, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. P.-N. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining. Addison Wesley, 1 edition, May 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. P. Vansteenwegen, W. Souffriau, G. V. Berghe, and D. V. Oudheusden. A guided local search metaheuristic for the team orienteering problem. European Journal of Operational Research, 196(1):118--127, July 2009.Google ScholarGoogle ScholarCross RefCross Ref
  14. B. Zhang and G. S. Sukhatme. Adaptive sampling with multiple mobile robots. In IEEE International Conference on Robotics and Automation, 2008.Google ScholarGoogle Scholar

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      • Published in

        cover image ACM Conferences
        GIS '09: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
        November 2009
        575 pages
        ISBN:9781605586496
        DOI:10.1145/1653771

        Copyright © 2009 ACM

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        New York, NY, United States

        Publication History

        • Published: 4 November 2009

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