Compressive mobile sensing for robotic mapping

4th IEEE Conference on Automation Science and Engineering, CASE 2008

Volumn , Issue , 2008, Pages 139-144

  Hu, Sheng ^a   Tan, Jindong ^a  

^a Michigan Technological University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONFORMAL MAPPING; ROBOTICS; SENSOR NETWORKS; SIGNAL RECONSTRUCTION;

COMPRESSIVE SENSING; EFFICIENT; LINEAR MEASUREMENTS; MEASUREMENT PROCESSES; MOBILE SENSING; MOBILE SENSORS; MULTIPLE SENSORS; ORTHOGONAL BASES; RESEARCH FIELDS; ROBOTIC MAPPINGS; SENSING FIELDS; SHANNON SAMPLINGS; SPARSE SIGNALS;

SENSORS;

EID: 54949121822     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/COASE.2008.4626560     Document Type: Conference Paper

References (18)

1. J. Haupt, W. Bajwa, M. Rabbat, and R. Nowak. Compressed sensing for networked data. to appear in the IEEE Signal Processing Magazine, Dec 2007.
2. E.J. Candes, J. Romberg, and T. Tao. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. Information Theory, IEEE Transactions on, 52(2):489-509, Feb 2006. ISSN 0018-9448.
3. D.L. Donoho. Compressed sensing. Information Theory, IEEE Transactions on, 52(4): 1289-1306, April 2006. ISSN 0018-9448.
4. D. W. Cho. Certainty grid representation for robot navigation by a bayesian method. ROBOTICA, 8:159-165, 1990.
5. J. H. Lim and D. W. Cho. Multipath bayesian map construction model from sonar data. ROBOTICA, 14:527-540, 1996.
6. R. G. Baraniuk, M. Davenport, R. A. DeVore, and M. B. Wakin. A simplied proof of the restricted isometry property for random matrices (aka the johnson-lindenstrauss lemma meets compressed sensing). Constructive Approximation, 2007.
7. Radu Berinde and Piotr Indyk. Sparse recovery using sparse matrices. Technical report, Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory. Jan 10 2008.
8. Richard Baraniuk and Philippe Steeghs. Compressive radar imaging. Radar Conference, 2007 IEEE, pages 128-133, 17-20 April 2007. ISSN 1097-5659.
9. M.F. Duarte, S. Sarvotham, D. Baron, M.B. Wakin, and R.G. Baraniuk. Distributed compressed sensing of jointly sparse signals. Signals, Systems and Computers, 2005. Conference Record of the Thirty-Ninth Asilomar Conference on, pages 1537-1541, October 28 - November 1, 2005. ISSN 1058-6393.
10. Shihao Ji and Lawrence Carin. Bayesian compressive sensing and projection optimization. In ICML '07: Proceedings of the 24th international conference on Machine learning, pages 377-384. ACM, New York, NY, USA, 2007. ISBN 978-1-59593-793-3.
11. Scott Shaobing Chen, David L. Donoho, and Michael A. Saunders. Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing, 20(1):33-61, 1999.
12. E. J. Candes, M. Wakin, and S Boyd. Enhancing sparsity by reweighted 11 minimization. Technical report, California Institute of Technology, 2007.
13. R. Chartrand. Exact reconstruction of sparse signals via nonconvex minimization. Signal Processing Letters, IEEE, 14(10):707-710, Oct. 2007. ISSN 1070-9908.
14. J.A. Tropp. Greed is good: algorithmic results for sparse approximation. Information Theory, IEEE Transactions on, 50(10):2231-2242, Oct. 2004. ISSN 0018-9448.
15. Chinh La and M.N. Do. Tree-based orthogonal matching pursuit algorithm for signal reconstruction. Image Processing, 2006 IEEE International Conference on, pages 1277-1280, 8-11 Oct. 2006. ISSN 1522-4880.
16. R.G. Baraniuk. Compressive sensing [lecture notes]. Signal Processing Magazine, IEEE, 24(4): 118-121, July 2007. ISSN 1053-5888.
17. E. Candes and J. Romberg. Practical signal recovery from random projections. IEEE Trans. Signal Processing, 2005. ISBN 1-59593-334-4. Preprint.
18. B.D. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. In International Joint Conference on Artificial Intelligence, pages 674-679, 1981.