Abstract
This paper introduces an approach for on-line marker-based three dimensional modeling with scale estimation and heightmap construction from monocular images. The presented system is also capable of an off-line marker-less 3D reconstruction from monocular images with increased detail. This method is designed for the flexible use with an Unmaned Aerial Vehicle (UAV); this means that, despite being tested with a Parrot AR.Drone 1.0, it is easily portable to other more capable UAV models. The followed approach was an adaptation of the patch-based Multiview Stereo (PMVS) algorithm for on line point cloud generation. The system achieved 1.05 processed images per second on average, slightly surpassing the planed objective of 1 processed image per second. The height estimation error ranges between 1-1.5% with a manual marker detection and 4-5% with automatic marker detection, which seems accurate enough for autonomous navigation and path planning. As future work, tests with a better UAV, processing time reduction, marker-less height map construction, autonomous indoor navigation and collaborative on-line 3D modeling are planned.
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References
Gertler, J.: Us unmanned aerial systems. Congressional Research Service (2012)
Cetinsoy, E., Dikyar, S., Hancer, C., Oner, K., Sirimoglu, E., Unel, M., Aksit, M.: Design and construction of a novel quad tilt-wing UAV. Mechatronics 22, 723–745 (2012)
Sanfourche, M., Delaune, J., Besnerais, G.L.: Perception for UAV: Vision-based navigation and environment modeling. Journal AerospaceLab 4, 1–19 (2012)
Sirmacek, B., d’Angelo, P., Krauss, T., Reinartz, P.: Enhancing urban digital elevation models using automated computer vision techniques. In: International Conference on Pattern Recognition (2010)
Weibel, R., Heller, M.: Digital Terrain Modelling (1993)
Call, B.: Obstacle avoidance for unmanned air vehicles. Master’s thesis, Brigham Young University (2006)
Besnerais, G., Sanfourche, M., Champagnat, F.: Dense height map estimation from oblique aerial image sequences. Computer Vision and Image Understanding 109, 204–225 (2008)
Bills, C., Chen, J., Saxena, A.: Autonomous MAV flight in indoor environments using single image perspective cues. In: International Conference on Robotics and Automation, ICRA (2011)
Jimenez-Lugo, J., Zell, A.: Framework for autonomous onboard navigation with the AR.Drone. In: International Conference on Unmanned Aircraft Systems, pp. 575–583 (2013)
Engel, J., Sturmand, J., Cremers, D.: Camera-based navigation of a low-cost quadrocopter. In: 2012 IEEE/RSJ International Intelligent Robots and Systems (IROS), vol. 320 (2012)
Pollefeys, M., Nistér, D., et al.: Detailed real-time urban 3d reconstruction from video. International Journal of Computer Vision 78, 143–167 (2008)
Wen-Chung, C., Shu-An, L.: Real-time feature-based 3d map reconstruction for stereo visual guidance and control of mobile robots in indoor environments. In: 2004 IEEE International Conference on Systems, Man and Cybernetics, vol. 6, pp. 5386–5391 (2008)
Diskin, Y., Asari, V.: Dense point-cloud creation using superresolution for a monocular 3d reconstruction system. In: Proc. SPIE 8399, Visual Information Processing XXI, pp. 83990–N–83990N–9 (2012)
Motta, C.: Reconstrucc in tridimensional de fachadas de edificios empleando imgenes monoculares obtenidas por un veculo areo no tripulado autnomo. Master’s thesis, Centro de Investigacin y de Estudios Avanzados del Instituto Politcnico Nacional (February 2014)
Scaramuzza, D., Fraundorfer, F., Pollefeys, M., Siegwart, R.: Absolute scale in structure from motion from a single vehicle mounted camera by exploiting nonholonomic constraints. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1413–1419 (2009)
Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F., Marín-Jiménez, M.: Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognition 47(6), 2280–2292 (2014)
Furukawa, Y., Ponce, J.: Accurate, dense, and robust multi-view stereopsis. Pattern Analysis and Machine Intelligence 32(8), 1362–1376 (2010)
Moulon, P., Monasse, P., Marlet, R.: Adaptive structure from motion with a contrario model estimation. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part IV. LNCS, vol. 7727, pp. 257–270. Springer, Heidelberg (2013)
Barazzetti, L., Scaioni, M.: Orientation and 3D modelling from markerless terrestrial images: Combining accuracy with automation. The Photogrammetric Record, vol. 25, p. 2010 (2013)
Weiss, S., Achtelik, M., Kneip, L., Scaramuzza, D., Siegwart, R.: Intuitive 3D maps for MAV terrain exploration and obstacle avoidance. Journal of Intelligent and Robotics Systems 61(1-4), 473–493 (2011)
Davison, A.: Real-time simultaneous localisation and mapping with a single camera. In: 2003 Proceedings of the Ninth IEEE International Conference on Computer Vision, vol. 2, pp. 1403–1141 (2003)
Mian, A., Bennamoun, M., Owens, R.: On the repeatability and quality of keypoints for local feature-based 3d object retrieval from cluttered scenes. International Journal of Computer Vision 89, 348–361 (2010)
Chen, H., Bhanu, B.: 3d free-form object recognition in range images using local surface patches. Pattern Recognition Letters 28, 1252–1262 (2007)
Zhong, Y.: Intrinsic shape signatures: A shape descriptor for 3d object recognition. In: Proc. 3DRR Workshop (in conj. with ICCV), pp. 689–696 (2009)
Lamdan, Y., Wolfson, H.: Geometric hasing: A general and efficient model-based recognition scheme. In: Proc. Int. Conf. on Computer Vision, pp. 238–249 (1988)
Gwimson, W., Huttenlocker, D.: On the sensitivity of geometric hashing. In: Proc. Int. Conf. on Computer Vision, pp. 334–338 (1990)
Lamdan, Y., Wolfson, H.: On the error analysis of geometric hashing. In: Proc. IEEE Conf. on Computer Vision, pp. 22–27 (1991)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, pp. 147–151 (1988)
Marr, D., Hildreth, E.: Theory of edge detection. In: Proceedings of the Royal Society of London, pp. 215–217 (1980)
Tombari, F., Stefano, L.D.: Object recognition in 3d scenes with occlusions and clutter by hough voting. In: 4th Pacific-Rim Symposium on Image and Video Technology, pp. 349–355 (2010)
Hough, P.: Method and means for recognizing complex patterns (1962)
Khoshelham, K.: Extending generalized hough transform to detect 3d objects in laser range data. In: Proc. ISPRS Workshop on Laser Scanning, pp. 206–210 (2007)
Tombari, F., Salti, S., DiStefano, L.: Unique signatures of histograms for local surface description. In: ECCV 2013 Proceedings of the 11th European Conference on Computer Vision Conference on Computer vision, pp. 356–369 (2010)
Petrelli, A., Stefano, L.D.: On the repeatability of the local reference frame for partial shape matching. In: 13th International Conference on Computer Vision (ICCV), pp. 2244–2251 (2011)
Lowe, D.: Distinctive image features from scale-invariant key points. International Journal of Computer Vision 60(2), 91–110 (2004)
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Larranaga-Cepeda, A., Ramirez-Torres, J.G., Motta-Avila, C.A. (2014). On-Line Dense Point Cloud Generation from Monocular Images with Scale Estimation. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds) Human-Inspired Computing and Its Applications. MICAI 2014. Lecture Notes in Computer Science(), vol 8856. Springer, Cham. https://doi.org/10.1007/978-3-319-13647-9_34
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DOI: https://doi.org/10.1007/978-3-319-13647-9_34
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