4D crop monitoring: Spatio-temporal reconstruction for agriculture

Proceedings - IEEE International Conference on Robotics and Automation

Volumn , Issue , 2017, Pages 3878-3885

  Dong, Jing ^a   Burnham, John Gary ^b   Boots, Byron ^a   Rains, Glen ^b   Dellaert, Frank ^a  

^a Georgia Institute of Technology   (United States)

^b UNIVERSITY OF GEORGIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AGRICULTURE; CROPS; ROBOTICS;

CRITICAL PROBLEMS; GEOMETRIC PROPERTIES; PRECISION AGRICULTURE; SPATIAL AND TEMPORAL RESOLUTIONS; SPATIO-TEMPORAL MODELS; SPATIO-TEMPORAL RECONSTRUCTION; STRUCTURE FROM MOTION; VISUAL APPEARANCE;

STEREO IMAGE PROCESSING;

EID: 85028018412     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICRA.2017.7989447     Document Type: Conference Paper

References (51)

1. F. Rembold, C. Atzberger, I. Savin, and O. Rojas, "Using low resolution satellite imagery for yield prediction and yield anomaly detection", Remote Sensing, vol. 5, no. 4, pp. 1704-1733, 2013.
2. M. Bryson, A. Reid, F. Ramos, and S. Sukkarieh, "Airborne visionbased mapping and classification of large farmland environments", J. of Field Robotics, vol. 27, no. 5, pp. 632-655, 2010.
3. D. Anthony, S. Elbaum, A. Lorenz, and C. Detweiler, "On crop height estimation with UAVs", in IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2014.
4. J. Das, G. Cross, C. Qu, A. Makineni, P. Tokekar, Y. Mulgaonkar, and V. Kumar, "Devices, systems, and methods for automated monitoring enabling precision agriculture", in IEEE Intl. Conf. on Automation Science and Engineering (CASE), 2015.
5. K. Zainuddin, M. Jaffri, M. Zainal, N. Ghazali, and A. Samad, "Verification test on ability to use low-cost UAV for quantifying tree height", in IEEE Intl. Colloquium on Signal Processing & Its Applications (CSPA), 2016.
6. S. K. Sarkar, J. Das, R. Ehsani, and V. Kumar, "Towards autonomous phytopathology: Outcomes and challenges of citrus greening disease detection through close-range remote sensing", in IEEE Intl. Conf. on Robotics and Automation (ICRA), 2016.
7. T. Hague, N. Tillett, and H. Wheeler, "Automated crop and weed monitoring in widely spaced cereals", Precision Agriculture, vol. 7, no. 1, pp. 21-32, 2006.
8. J.-F. Lalonde, N. Vandapel, and M. Hebert, "Automatic three-dimensional point cloud processing for forest inventory", Robotics Institute, p. 334, 2006.
9. S. Singh, M. Bergerman, J. Cannons, B. Grocholsky, B. Hamner, G. Holguin, L. Hull, V. Jones, G. Kantor, H. Koselka, et al., "Comprehensive automation for specialty crops: Year 1 results and lessons learned", Intelligent Service Robotics, vol. 3, no. 4, pp. 245-262, 2010.
10. S. Nuske, K. Wilshusen, S. Achar, L. Yoder, S. Narasimhan, and S. Singh, "Automated visual yield estimation in vineyards", J. of Field Robotics, vol. 31, no. 5, pp. 837-860, 2014.
11. J. P. Underwood, G. Jagbrant, J. I. Nieto, and S. Sukkarieh, "Lidarbased tree recognition and platform localization in orchards", J. of Field Robotics, vol. 32, no. 8, pp. 1056-1074, 2015.
12. D. Font, M. Tresanchez, D. Martínez, J. Moreno, E. Clotet, and J. Palacín, "Vineyard yield estimation based on the analysis of high resolution images obtained with artificial illumination at night", Sensors, vol. 15, no. 4, pp. 8284-8301, 2015.
13. I. Sa, Z. Ge, F. Dayoub, B. Upcroft, T. Perez, and C. McCool, "Deepfruits: A fruit detection system using deep neural networks", Sensors, vol. 16, no. 8, p. 1222, 2016.
14. S. Agarwal, N. Snavely, I. Simon, S. M. Seitz, and R. Szeliski, "Building Rome in a day", in Intl. Conf. on Computer Vision (ICCV), 2009.
15. Y. Furukawa and J. Ponce, "Accurate, dense, and robust multi-view stereopsis", IEEE Trans. Pattern Anal. Machine Intell., vol. 32, no. 8, pp. 1362-1376, 2010.
16. K. Sakurada, T. Okatani, and K. Deguchi, "Detecting changes in 3D structure of a scene from multi-view images captured by a vehiclemounted camera", in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2013.
17. P. F. Alcantarilla, S. Stent, G. Ros, R. Arroyo, and R. Gherardi, "Streetview change detection with deconvolutional networks", in Robotics: Science and Systems (RSS), 2016.
18. W. Xiao, B. Vallet, M. Brédif, and N. Paparoditis, "Street environment change detection from mobile laser scanning point clouds", ISPRS Journal of Photogrammetry and Remote Sensing, vol. 107, pp. 38-49, 2015.
19. T. Pollard and J. L. Mundy, "Change detection in a 3-D world", in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2007.
20. A. O. Ulusoy and J. L. Mundy, "Image-based 4-D reconstruction using 3-D change detection", in European Conf. on Computer Vision (ECCV), 2014.
21. R. Martin-Brualla, D. Gallup, and S. M. Seitz, "Time-lapse mining from internet photos", in ACM SIGGRAPH, 2015.
22. R. Martin-Brualla, D. Gallup, and S. M. Seitz, "3D time-lapse reconstruction from internet photos", in Intl. Conf. on Computer Vision (ICCV), 2015.
23. G. Schindler, F. Dellaert, and S. Kang, "Inferring temporal order of images from 3D structure", in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2007.
24. G. Schindler and F. Dellaert, "Probabilistic temporal inference on reconstructed 3d scenes", in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2010.
25. K. Matzen and N. Snavely, "Scene chronology", in European Conf. on Computer Vision (ECCV), 2014.
26. A. O. Ulusoy, O. Biris, and J. L. Mundy, "Dynamic probabilistic volumetric models", in Intl. Conf. on Computer Vision (ICCV), 2013.
27. S. Griffith, F. Dellaert, and C. Pradalier, "Robot-enabled lakeshore monitoring using visual SLAM and SIFT flow", in RSS Workshop on Multi-View Geometry in Robotics, Citeseer, 2015.
28. S. Griffith and C. Pradalier, "Reprojection flow for image registration across seasons", in British Machine Vision Conf. (BMVC), 2016.
29. M. J. Milford and G. F. Wyeth, "SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights", in IEEE Intl. Conf. on Robotics and Automation (ICRA), 2012.
30. T. Naseer, B. Suger, M. Ruhnke, and W. Burgard, "Vision-based markov localization across large perceptual changes", in European Conf. on Mobile Robots (ECMR), 2015.
31. S. Lowry, N. Sünderhauf, P. Newman, J. J. Leonard, D. Cox, P. Corke, and M. J. Milford, "Visual place recognition: A survey", IEEE Trans. Robotics, vol. 32, no. 1, pp. 1-19, 2016.
32. C. Beall and F. Dellaert, "Appearance-based localization across seasons in a metric map", IROS Workshop on Planning, Perception and Navigation for Intelligent Vehicles (PPNIV), 2014.
33. M. Stein, S. Bargoti, and J. Underwood, "Image based mango fruit detection, localisation and yield estimation using multiple view geometry", Sensors, vol. 16, no. 11, p. 1915, 2016.
34. D. Lowe, "Distinctive image features from scale-invariant keypoints", Intl. J. of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
35. M. Muja and D. G. Lowe, "Scalable nearest neighbor algorithms for high dimensional data", IEEE Trans. Pattern Anal. Machine Intell., vol. 36, no. 11, pp. 2227-2240, 2014.
36. R. I. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision. Cambridge University Press, second ed., 2004.
37. F. Dellaert and M. Kaess, "Square Root SAM: Simultaneous localization and mapping via square root information smoothing", Intl. J. of Robotics Research, vol. 25, pp. 1181-1203, Dec. 2006.
38. L. Carlone, Z. Kira, C. Beall, V. Indelman, and F. Dellaert, "Eliminating conditionally independent sets in factor graphs: A unifying perspective based on smart factors", in IEEE Intl. Conf. on Robotics and Automation (ICRA), 2014.
39. C. Forster, L. Carlone, F. Dellaert, and D. Scaramuzza, "IMU preintegration on manifold for efficient visual-inertial maximum-a-posteriori estimation", Robotics: Science and Systems (RSS), 2015.
40. S. Anderson and T. D. Barfoot, "Full STEAM ahead: Exactly sparse gaussian process regression for batch continuous-time trajectory estimation on SE (3)", IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2015.
41. T. Barfoot, C. H. Tong, and S. Sarkka, "Batch continuous-time trajectory estimation as exactly sparse gaussian process regression", Robotics: Science and Systems (RSS), 2014.
42. X. Yan, V. Indelman, and B. Boots, "Incremental sparse GP regression for continuous-time trajectory estimation & mapping", Proc. of the Intl. Symp. of Robotics Research (ISRR), 2015.
43. M. Kaess, H. Johannsson, R. Roberts, V. Ila, J. Leonard, and F. Dellaert, "iSAM2: Incremental smoothing and mapping using the Bayes tree", Intl. J. of Robotics Research, vol. 31, pp. 217-236, Feb. 2012.
44. V. Indelman, E. Nelson, J. Dong, N. Michael, and F. Dellaert, "Incremental distributed inference from arbitrary poses and unknown data association: Using collaborating robots to establish a common reference", IEEE Control Systems, vol. 36, no. 2, pp. 41-74, 2016.
45. C. Wu, B. Clipp, X. Li, J.-M. Frahm, and M. Pollefeys, "3d model matching with viewpoint-invariant patches (VIP)", in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 1-8, 2008.
46. H. Badino, D. Huber, and T. Kanade, "The CMU visual localization data set." http://3dvis.ri.cmu.edu/data-sets/localization, 2011.
47. M. F. Fallon, H. Johannsson, M. Kaess, D. M. Rosen, E. Muggler, and J. J. Leonard, "Mapping the MIT stata center: Large-scale integrated visual and RGB-D SLAM", in RSS Workshop on RGB-D: Advanced Reasoning with Depth Cameras, 2012.
48. N. Carlevaris-Bianco, A. K. Ushani, and R. M. Eustice, "University of Michigan north campus long-term vision and lidar dataset", Intl. J. of Robotics Research, vol. 35, no. 9, pp. 1023-1035, 2016.
49. F. Pomerleau, F. Colas, R. Siegwart, and S. Magnenat, "Comparing ICP variants on real-world data sets", Autonomous Robots, vol. 34, no. 3, pp. 133-148, 2013.
50. Y. Li, X. Fan, N. J. Mitra, D. Chamovitz, D. Cohen-Or, and B. Chen, "Analyzing growing plants from 4D point cloud data", ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia, vol. 32, no. 6, p. 157, 2013.
51. L. Carlone, J. Dong, S. Fenu, G. Rains, and F. Dellaert, "Towards 4D crop analysis in precision agriculture: Estimating plant height and crown radius over time via expectation-maximization", in ICRA Workshop on Robotics in Agriculture, 2015.