Finding the location of feature points in 3D space from 2D vision data in structured environments has been done successfully for years and has been applied effectively on industrial robots. Miniature flying robots flying in unknown environments have stringent weight, space, and security constraints. For such vehicles, it has been attempted here to reduce the number of vision sensors to a single camera. At first, feature points are detected in the image using Harris corner detector, the measurements of which are then statistically corresponded across various images, using knowledge of vehicle’s pose from onboard inertial measurement unit. First approach attempted is that of ego-motion perpendicular to camera axis and acceptable results for 3D feature point locations have been achieved. Next, except for a small region around the focus of expansion, forward translations along the camera axis have also been attempted with acceptable results, which is an improvement to the previous relevant work. The 3D location map of feature points thus obtained is utilizable for trajectory planning while ensuring collision avoidance through 3D space. Reduction of vision sensors to a single camera while utilizing minimum ego-motion space for 3D feature point location is a significant contribution of this work.
PrietoF. A CAD-based 3D data acquisition strategy for inspection. Mach Vision Appl2003; 15: 76–91.
2.
WeishiCHuanshengNJingL. Flying bird detection and hazard assessment for avian radar system. J Aerosp Eng2012; 25: 246–255.
3.
Coombs D and Roberts K. Bee-Bot. Using peripheral optical flow to avoid obstacles. In: Intelligent Robots and Computer Vision XI, SPIE, 1992, vol. 1825, pp.714–721.
4.
HowieCDavidK. Path planning and control for free-flying inspection robot in space. J Aerosp Eng1999; 12: 74–81.
5.
Argyros A and Bergholm F. Combining central and peripheral vision for reactive robot navigation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Colorado, 21–25 June 1999, pp.646–651.
6.
MalassiotisSStrintzisM. Stereo vision system for precision dimensional inspection of 3D holes. Mach Vision Appl2003; 15: 101–113.
Takeno J, Shinogi Y, Nishiyama S, et al. Realization of a 3D vision mobile robot that can avoid collision with moving obstacles. In: IEEE International Conference on Robotics and Automation, Sacramento, USA, 9–11 April 1991, vol. 3, pp.2010–2023.
9.
Langer D and Jochem T. Fusing radar and vision for detecting, classifying and avoiding roadway obstacles. In: IEEE Intelligent Vehicles Symposium, Japan, 18–20 September 1996, pp.333–338.
10.
KimJSukkariehS. Autonomous airborne navigation in unknown terrain environments. IEEE Trans Aerosp Electron Syst2004; 40(3): 1031–1045.
11.
Langelaan JW. State estimation for autonomous flight in cluttered environments. PhD Dissertation submitted to the department of Aeronautics and Astronautics, Stanford University, 2006.
12.
Mourikis AI and Roumeliotis SI. A multi-state constraint Kalman filter for vision-aided inertial navigation. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Rome, Italy, 10–14 April 2007, pp.3565–3572.
13.
Koch A, Wittich H and Thielecke F. A vision-based navigation algorithm for a VTOL-UAV. In: AIAA Guidance, Navigation, and Control Conference, Keystone, CO, August 2006.
14.
Call B, Beard R, Taylor C, et al. Obstacle avoidance for unmanned air vehicles using image feature tracking. In: AIAA Guidance, Navigation, and Control Conference, Keystone, CO, August 2006.
Wu AD and Johnson EN. Methods for localization and mapping using vision and inertial sensors. In: AIAA Guidance, Navigation and Control Conference and Exhibit, Honolulu, Hawaii, 18–21 August 2008.
17.
Ivey GF and Johnson EN. Investigation of methods for simultaneous localization and mapping using vision sensor. In: AIAA Guidance, Navigation, and Control Conference, Keystone, CO, August 2006.
18.
Toupet O, Paduano JD, Panish R, et al. Augmenting state estimates with multiple camera visual measurements. In: AIAA Infotech Conference, Rohnert Park, CA, May 2007.
19.
Watkins AS, Kehoe JJ and Lind R. SLAM for flight through urban environments using dimensionality reduction. In: AIAA Guidance, Navigation, and Control Conference, Keystone, CO, August 2006.
20.
Prazenica RJ, Watkins A, Kurdila AJ, et al. Vision-based Kalman filtering for aircraft state estimation and structure from motion. In: AIAA Guidance, Navigation, and Control Conference, San Francisco, CA, August 2005.
21.
Bao SY, Bagra M, Chao YW, et al. Semantic structure from motion with points, regions, and objects. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition CVPR, Rhode Island, 16–21 June 2012, pp.2703–2710.
22.
Brostow GJ, Shotton J, Fauqueur J, et al. Segmentation and recognition using structure from motion point clouds. In: Computer Vision ECCV, 2008, vol. 5302, pp.44–57.
23.
Garg R, Seitz S, Ramanan D, et al. Where’s Waldo: Matching people in images of crowds. In: IEEE CVPR, 2011, Colorado Springs, 20–25 June 2011, pp.1793–1800.
24.
Gupta A, Efros A and Hebert M. Blocks world revisited: Image understanding using qualitative geometry and mechanics. In: Computer Vision ECCV, Greece, 5–11 September 2010, vol. 6314/2010, pp.482–496.
25.
Lee D, Hebert M and Kanade T. Geometric reasoning for single image structure recovery. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Florida, 20–21 June 2009, vol. 6367.
26.
Payet N and Todorovic S. From contours to 3D object detection and pose estimation. In: Proc. IEEE Int. Conf. Computer Vision (ICCV 2011), Barcelona, Spain, 2011.
27.
Nelson R. Visual navigation. PhD Thesis, University of Maryland, 1988.
28.
Nelson R and Aloimonos J. Using flow field divergence for obstacle avoidance towards qualitative vision. In: IEEE Second International Conference on Computer Vision, Florida, USA, 5–8 December 1988, pp.188–96.
29.
NelsonRAloimonosJ. Obstacle avoidance using flow field divergence. IEEE Trans PAMI1989; 11(10): 1102–1106.
30.
Young G, Hong T, Herman M, et al. Obstacle detection for a vehicle using optical flow. In: Proceedings of the Intelligent Vehicles '92 Symposium, Detroit, 29 June—1 July 1992, pp.185–190.
31.
Hatsopoulos N and Anderson J. Collision-avoidance system based on optical flow. In: Proceedings of the Intelligent Vehicles '92 Symposium, Detroit, 29 June–1 July 1992, pp.79–84.
32.
Ilic M, Masciangelo S and Pianigiani E. Ground plane obstacle detection from optical flow anomalies: A robust and efficient implementation. In: IEEE Intelligent Vehicles Symposium, Proceedings, Paris, 24–26 October 1994, pp.333–338.
33.
SchellF-RDickmannsE. Autonomous landing of airplane by dynamic machine vision. Mach Vision Appl1994; 7: 127–134.
34.
Nakao K, Kondo K, Kobashi S, et al. Shape reconstruction using extended Kalman filter with an active camera. In: IEEE International Symposium on Communications and Information Technologies, Korea, 28–30 September 2004, vol. 2, pp.857–860.
35.
TsaiR. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Trans Robotics Autom1987; 3(4): 323–344.
36.
JohnsonESchrageD. System integration and operation of a research unmanned aerial vehicle. AIAA J Aerosp Comput Inform Commun2004; 1(1): 5–18.
37.
Ha J, Johnson E and Tannenbaum A. Real-time visual tracking system using geometric active contours for navigation and control of UAVs. In: Proceedings of the American Control Conference, New York, 9–13 July 2007, pp.365–370.
38.
Watanabe Y, Johnson E and Calise A. Stochastically optimized monocular vision-based guidance design. In: AIAA Guidance, Navigation, and Control Conference, South Carolina, USA, 20–23 August 2007, pp.5349–5364.
39.
Watanabe Y. Stochastically optimized monocular vision-based navigation and guidance. PhD Thesis, Georgia Institute of Technology, 2008.
40.
Watanabe Y, Johnson E and Calise A. Vision-based approach to obstacle avoidance. In: Proceedings of the AIAA Guidance, Navigation, and Control Conference, San Francisco, August 2005, vol. 6092. pp.1–10.
41.
HayterA. Probability and Statistics for Engineers and Scientists20012ed. Duxbury Pr Pacific Grove CA 93950 USA. ISBN 0534386695.
42.
GonzalezRWoodsREddinsS. Digital Image Processing Using Matlab, Upper Saddle River, New Jersey USA: Pearson Prentice-Hall, 2004.
43.
MatthiesLKanadeTSzeliskiR. Kalman filter-based algorithms for estimating depth from image sequences. Int J Comput Vision1989; 3(3): 209–238.
44.
RibeiroMI.. Kalman and Extended Kalman Filters: Concept, Derivation and Properties, Portugal: Institute for Systems and Robotics, 2004, pp. 31–41.
45.
Wu A. Vision-based navigation and mapping for flight in GPS-denied environments. PhD Thesis, Georgia Institute of Technology, 2010.
46.
JohnsonENKannanSK. Adaptive trajectory control for autonomous helicopters. AIAA J Guid Control Dynamics2005; 28: 524/538–524/538.
47.
Johnson EN, Schrage DP and Vachtsevansos G. Software enabled control experiments with university-operated unmanned aircraft. In: Infotech@Aerospace, American Institute of Aeronautics and Astronautics, Arlington, VA, September 2006.
