Cost-effective mapping using unmanned aerial vehicles in ecology monitoring applications

Springer Tracts in Advanced Robotics

Volumn 79, Issue , 2014, Pages 509-523

  Bryson, Mitch ^a   Reid, Alistair ^a   Hung, Calvin ^a   Ramos, Fabio Tozeto ^a   Sukkarieh, Salah ^a  

^a UNIVERSITY OF SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COST EFFECTIVENESS; COSTS; ECOLOGY; MAPPING; REMOTE SENSING; ROBOTICS;

DATA COLLECTION; ENVIRONMENT MAPPING; EXPERIMENTAL EVALUATION; LOW-COST SENSORS; MONITORING APPLICATIONS; MONOCULAR VISION; SATELLITE REMOTE SENSING; VEGETATION CLASSIFICATION;

UNMANNED AERIAL VEHICLES (UAV);

EID: 84928230747     PISSN: 16107438     EISSN: 1610742X     Source Type: Book Series    
DOI: 10.1007/978-3-642-28572-1_35     Document Type: Conference Paper

References (23)

1. Barber, C.B., Dobkin, D.P., Huhdanpaa, H.T.: The Quickhull algorithm for convex hulls. ACM Trans. on Mathematical Software 22(4), 469-483 (1996)
2. Bouguet, J.: Camera Calibration Toolbox for MATLAB, http://www.vision.caltech.edu/bouguetj/calib_doc?(retrieved September 7, 2010)
3. Bryson, M., Reid, A., Ramos, F., Sukkarieh, S.: Airborne Vision-Based Mapping and Classification of Large Farmland Environments. Journal of Field Robotics 27(5), 632-655 (2010)
4. Bryson, M., Sukkarieh, S.: A Comparison of Feature and Pose-Based Airborne Mapping using Monocular Vision and Inertial Sensors. IEEE/RSJ International Conference on Intelligent Robots and Systems (2011)
5. Clark, R.R., Lin, M.H., Taylor, C.J.: 3D Environment Capture from Monocular Video and Inertial Data. In: SPIE on Three-Dimensional Image Capture and Applications (2006)
6. Davis, T.: Direct Methods for Sparse Linear Systems. Fundamentals of Algorithms. SIAM, Philadelphia (2006)
7. Eustice, R., Singh, H., Leonard, J., Walter, M., Ballard, R.: Visually Navigating the RMS Titanic with SLAM Information Filters. In: Robotics: Science and Systems (2005)
8. Fan, R.E., Chen, P.H., Lin, C.J.:Working set selection using the second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918 (2005)
9. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Annals of Statistics 28(2), 337-407 (2000)
10. Heeger, D., Bergen, J.: Pyramid-based texture analysis/synthesis. In: Proceedings of the 22nd International Conference on Computer Graphics and Interactive Techniques (1995)
11. Hsieh, P., Lee, L., Chen, N.: Effect of spatial resolution on classification errors of pure and mixed pixels in remote sensing. IEEE Transactions on Geoscience and Remote Sensing 39(12), 2657-2663 (2001)
12. Hung, C., Bryson, M., Sukkarieh, S.: Multi-class Predictive Template for Tree Crown Detection. ISPRS Journal of Photogrammetry and Remote Sensing 68, 170-183 (2012)
13. Kaess, M., Ranganathan, A., Dallaert, F.: iSAM: Fast Incremental Smoothing and Mapping with Efficient Data Association. In: IEEE International Conference on Robotics and Automation (2007)
14. Klinken, R., Shepherd, D., Parr, R., Robinson, T., Anderson, L.: Mapping mesquite (prosopis) distribution and density using visual aerial surveys. Rangeland Ecology Management 60, 408-416 (2007)
15. van Klinken, R.D., Shepherd, D., Parr, R., Robinson, T.P., Anderson, L.: Mapping Mesquite (Prosopis) Distribution and Density Using Visual Aerial Surveys. Rangeland Ecology Management 60, 408-416 (2007)
16. Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91-110 (2004)
17. Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Imaging Understanding Workshop (1981)
18. Maybeck, P.S.: Stochastic Models, Estimation and Control, vol. 1. Academic Press (1979)
19. Medlin, C.R., Shaw, D.R., Gerard, P.D., LaMastus, F.E.: Using remote sensing to detect weed infestations in Glycine max. Weed Science 48(3), 393-398 (2000)
20. Mostafa, M., Schwarz, K.: A Multi-Sensor System for Airborne Image Capture and Georeferencing. Photogrammetric Engineering and Remote Sensing 66(12), 1417-1423 (2000)
21. Shi, J., Tomasi, C.: Good Features to Track. In: IEEE Conference on Computer Vision and Pattern Recognition (1994)
22. Torr, P.H.S., Murray, D.W.: The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix. International Journal of Computer Vision 24(3), 271-300 (1997)
23. Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle Adjustment-A Modern Synthesis. In: Triggs, B., Zisserman, A., Szeliski, R. (eds.) ICCV-WS 1999. LNCS, vol. 1883, pp. 298-372. Springer, Heidelberg (2000)