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Three 2D-warping schemes for visual robot navigation

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Abstract

Warping (Franz et al., Biological Cybernetics 79(3), 191–202, 1998b) and 2D-warping (Möller, Robotics and Autonomous Systems 57(1), 87–101, 2009) are effective visual homing methods which can be applied for navigation in topological maps. This paper presents several improvements of 2D-warping and introduces two novel “free” warping methods in the same framework. The free warping methods partially lift the assumption of the original warping method that all landmarks have the same distance from the goal location. Experiments on image databases confirm the effect of the improvements of 2D-warping and show that the two free warping methods produce more precise home vectors and approximately the same proportion of erroneous home vectors. In addition, two novel and easier-to-interpret performance measures for the angular error are introduced.

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References

  • Arena, P., De Fiore, S., Fortuna, L., Nicolosi, L., Patené, L., & Vagliasindi, G. (2007). Visual homing: Experimental results on and autonomous robot. In Proc. 18th European conf. on circuit theory and design (pp. 304–307). IEEE Xplore.

  • Argyros, A. A., Bekris, K. E., Orphanoudakis, S. C., & Kavraki, L. E. (2005). Robot homing by exploiting panoramic vision. Autonomous Robots, 19(1), 7–25.

    Article  Google Scholar 

  • Bailey, T., & Durrant-Whyte, H. (2006). Simultaneous localization and mapping (SLAM): Part II. IEEE Robotics & Automation Magazine, 13(3), 108–117.

    Article  Google Scholar 

  • Barron, J. L., Fleet, D. J., & Beauchemin, S. S. (1994). Performance of optical flow techniques. International Journal of Computer Vision, 12(1), 43–77.

    Article  Google Scholar 

  • Basri, R., Rivlin, E., & Shimshoni, I. (1999). Visual homing: Surfing on the epipoles. International Journal of Computer Vision, 33(2), 117–137.

    Article  Google Scholar 

  • Basten, K., & Mallot, H. A. (2010). Simulated visual homing in desert ant natural environments: efficiency of skyline cues. Biological Cybernetics, 102(5), 413–425.

    Article  Google Scholar 

  • Bekris, K. E., Argyros, A. A., & Kavraki, L. E. (2004). Angle-based methods for mobile robot navigation: Reaching the entire plane. In Proc. IEEE int. conf. on robotics and automation (pp. 2373–2378).

  • Binding, D., & Labrosse, F. (2006). Visual local navigation using warped panoramic images. In Proc. towards autonomous robotic systems. University of Surrey, Guildford, UK.

  • Booij, O., Terwijn, Z., Zivkovic, Z., & Kröse, B. (2007). Navigation using an appearance based topological map. In Proc. IEEE int. conf. on robotics and automation (pp. 3927–3932).

  • Booij, O., Zivkovic, Z., & Kröse, B. (2008). Sampling in image space for vision based SLAM. In Proc. inside data association workshop during the robotics: science and systems conf. (RSS).

  • Booij, O., Zivkovic, Z., & Kröse, B. (2009). Efficient data association for view based SLAM using connected dominating sets. Robotics and Autonomous Systems, 57(12), 1225–1234.

    Article  Google Scholar 

  • Brandt, N. (2007). Visuelle Navigation in veränderlicher Umgebung. Diploma thesis, Bielefeld University, Faculty of Technology, Computer Engineering Group.

  • Briggs, A., Li, Y., Scharstein, D., & Wilder, M. (2006a). Robot navigation using 1D panoramic images. In Proc. IEEE int. conf. on robotics and automation (ICRA 2006) (pp. 2679–2685), Orlando, FL, May 2006.

  • Briggs, A. J., Detweiler, C., Li, Y., Mullen, P. C., & Scharstein, D. (2006b). Matching scale-space features in 1D panoramas. Computer Vision and Image Understanding, 103(3), 184–195.

    Article  Google Scholar 

  • Bunschoten, R., & Kröse, B. (2003). Robust scene reconstruction from an omnidirectional vision system. IEEE Transactions on Robotics and Automation, 19(2), 351–357.

    Article  Google Scholar 

  • Burke, A., & Vardy, A. (2006). Visual compass methods for robot navigation. In Seventeenth annual Newfoundland electrical and computer engineering conf., IEEE Newfoundland and Labrador Section.

  • Cartwright, B. A., & Collett, T. S. (1983). Landmark learning in bees. Journal of Comparative Physiology A, 151(4), 521–543.

    Article  Google Scholar 

  • Cartwright, B. A., & Collett, T. S. (1987). Landmark maps for honeybees. Biological Cybernetics, 57(1–2), 85–93.

    Article  Google Scholar 

  • Cassinis, R., Duina, D., Inelli, S., & Rizzi, A. (2002). Unsupervised matching of visual landmarks for robotic homing using Fourier-Mellin transform. Robotics and Autonomous Systems, 40(2–3), 131–138.

    Article  Google Scholar 

  • Cassinis, R., Grana, D., & Rizzi, A. (1996). Using colour information in an omnidirectional perception system for autonomous robot localization. In Proc. EUROBOT’96 (pp. 172–176). Los Alamitos: IEEE Computer Society Press.

    Google Scholar 

  • Chen, Z., Samarabandu, J., & Rodrigo, R. (2007). Recent advances in simultaneous localization and map-building using computer vision. Advanced Robotics, 21(3–4), 233–265.

    Article  Google Scholar 

  • Churchill, D., & Vardy, A. (2008). Homing in scale space. In Proc. IEEE/RSJ int. conf. on intelligent robots and systems (IROS 2008). IEEE Xplore.

  • Colios, C. I., & Trahanias, P. E. (2001). A framework for visual landmark identification based on projective and point-mutation invariant vectors. Robotics and Autonomous Systems, 35(1), 37–51.

    Article  MATH  Google Scholar 

  • Cormen, T. H., Leiserson, C. E., Rivest, R. E., & Stein, C. (2003). Introduction to algorithms (2nd ed.). Cambridge: MIT Press.

    Google Scholar 

  • Cruse, H. (2003). A recurrent network for landmark-based navigation. Biological Cybernetics, 88(6), 425–437.

    MATH  Google Scholar 

  • Dai, D., & Lawton, D. T. (1993). Range-free qualitative navigation. In Proc. IEEE int. conf. on robotics and automation (pp. 783–790).

  • Davison, A. J. (2003). Real-time simultaneous localisation and mapping with a single camera. In Proc. int. conf. on computer vision (pp. 1403–1410).

  • Davison, A. J., & Kita, N. (2001). Sequential localisation and map-building for real-time computer vision and robotics. Robotics and Autonomous Systems, 36(4), 171–183.

    Article  Google Scholar 

  • Davison, A. J., & Murray, D. W. (2002). Simultaneous localization and map-building using active vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 865–880.

    Article  Google Scholar 

  • Davison, A. J., Reid, I. D., Molton, N. D., & Stasse, O. (2007). MonoSLAM: Real-time single camera SLAM. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), 1052–1067.

    Article  Google Scholar 

  • Durrant-Whyte, H., & Bailey, T. (2006). Simultaneous localization and mapping: Part I. IEEE Robotics & Automation Magazine, 13(2), 99–108.

    Article  Google Scholar 

  • Eade, E., & Drummond, T. (2009). Edge landmarks in monocular SLAM. Image and Vision Computing, 27(5), 588–596.

    Article  Google Scholar 

  • Efron, B., & Tibshirani, R. J. (1998). An Introduction to the Bootstrap. Boca Raton: Chapman & Hall/CRC.

    Google Scholar 

  • Esteban, I., Booij, O., Zivkovic, Z., & Krose, B. (2008). Mapping large environments with an omnivideo camera. In Proc. int. conf. on simulation, modelling and programming of autonomous robots (SIMPAR) (pp. 297–306).

  • Eustice, R. M., Singh, H., & Leonard, J. J. (2006). Exactly sparse delayed-state filters for view-based SLAM. IEEE Transactions on Robotics, 22(6), 1100–1114.

    Article  Google Scholar 

  • Fiala, M., & Basu, A. (2004). Robot navigation using panoramic tracking. Pattern Recognition, 37(11), 2195–2215.

    Article  Google Scholar 

  • Filliat, D., & Meyer, J.-A. (2003). Map-based navigation in mobile robots. I. A review of localization strategies. Cognitive Systems Research, 4(4), 243–282.

    Article  Google Scholar 

  • Fischer, S. (2006). Visuelle Navigation mit Parameter-Modellen. Diploma thesis, Bielefeld University, Faculty of Technology, Computer Engineering Group.

  • Franz, M. O. (1999). Minimalistic visual navigation. Düsseldorf: VDI-Verlag. Ph.D. thesis, University of Tübingen.

    Google Scholar 

  • Franz, M. O., & Mallot, H. A. (2000). Biomimetic robot navigation. Robotics and Autonomous Systems, special issue: Biomimetic Robots, 30(1–2), 133–153.

    Google Scholar 

  • Franz, M. O., Schölkopf, B., Mallot, H. A., & Bülthoff, H. H. (1998a). Learning view graphs for robot navigation. Autonomous Robots, 5(1), 111–125.

    Article  Google Scholar 

  • Franz, M. O., Schölkopf, B., Mallot, H. A., & Bülthoff, H. H. (1998b). Where did I take that snapshot? Scene-based homing by image matching. Biological Cybernetics, 79(3), 191–202.

    Article  MATH  Google Scholar 

  • Franz, M. O., Stürzl, W., Hübner, W., & Mallot, H. A. (2008). A robot system for biomimetic navigation—from snapshots to metric embeddings of view graphs. In M. E. Jefferies & W.-K. Yeap (Eds.), Springer tracts in advanced robotics : Vol. 38. Robotics and cognitive approaches to spatial mapping (pp. 297–314). Berlin: Springer. Chap. 14.

    Chapter  Google Scholar 

  • Gaussier, P., Joulain, C., Zrehen, S., Banquet, J., & Revel, A. (1997). Visual navigation in an open environment without map. In Proc. IEEE/RSJ int. conf. on intelligent robots and systems (IROS’97) (pp. 545–550).

  • Gaussier, P., Joulain, C., Banquet, J., Lepêtre, S., & Revel, A. (2000). The visual homing problem: An example of robotics/biology cross fertilization. Robotics and Autonomous Systems, 30(1–2), 155–180.

    Article  Google Scholar 

  • Gerstmayr, L., Röben, F., Krzykawski, M., Kreft, S., Venjakob, D., & Möller, R. (2009). A vision-based trajectory controller for autonomous cleaning robots. In R. Dillmann, J. Beyerer, C. Stiller, J. M. Zöllner, & T. Gindele (Eds.), Autonome mobile systeme 2009. Informatik Aktuell (pp. 65–72). Berlin: Springer.

    Chapter  Google Scholar 

  • Giachetti, A. (2000). Matching techniques to compute image motion. Image and Vision Computing, 18(3), 247–260.

    Article  Google Scholar 

  • Goedemé, T., Nuttin, M., Tuytelaars, T., & Van Gool, L. (2004). Vision-based intelligent wheel chair control: The role of vision and inertial sensing in topological navigation. Journal of Robotic Systems, 21(2), 85–94.

    Article  Google Scholar 

  • Goedemé, T., Tuytelaars, T., Van Gool, L., Vanhooydonck, D., Demeester, E., & Nuttin, M. (2005). Is structure needed for omnidirectional visual homing? In Proc. IEEE int. symposium on computational intelligence in robotics and automation, CIRA 2005 (pp. 303–308). IEEE Xplore.

  • Goedemé, T., Nuttin, M., Tuytelaars, T., & Van Gool, L. (2007). Omnidirectional vision based topological navigation. International Journal of Computer Vision, 74(3), 219–236.

    Article  Google Scholar 

  • Goldhoorn, A., Ramisa, A., de Mántaras, R. L., & Toledo, R. (2007). Using the average landmark vector method for robot homing. In Frontiers in artificial intelligence and applications : Vol. 163. Proc. conf. on artificial intelligence research and development (pp. 331–338). Amsterdam: IOS Press.

    Google Scholar 

  • Gourichon, S., Meyer, J.-A., & Pirim, P. (2002). Using colored snapshots for short-range guidance in mobile robots. International Journal of Robotics and Automation, 17(4), 154–162.

    Google Scholar 

  • Hafner, V. V. (2000). Cognitive maps for navigation in open environments. In Proc. 6th int. conf. on intelligent autonomous systems (IAS-6) (pp. 801–808). Venice: IOS Press.

    Google Scholar 

  • Hafner, V. V. (2001). Adaptive homing—robotic exploration tours. Adaptive Behavior, 9(4), 131–141.

    Article  Google Scholar 

  • Hafner, V. V. (2004). Adaptive navigation strategies in biorobotics: Visual homing and cognitive mapping in animals and machines. Aachen: Shaker Verlag. Ph.D. thesis, University of Zurich.

    Google Scholar 

  • Hafner, V. V., & Möller, R. (2001). Learning of visual navigation strategies. In M. Quoy, P. Gaussier, J. Wyatt (Eds.), Proc. European workshop of learning robots (EWLR-9) (pp. 47–56), Prague.

  • Harris, C., & Stephens, M. (1988). A combined corner and edge detector. In Proc. 4th alvey vision conf. (pp. 147–151).

  • Hong, J., Tan, X., Pinette, B., Weiss, R., & Riseman, E. (1992). Image-based homing. IEEE Control Systems Magazin, 12(1), 38–45.

    Article  Google Scholar 

  • Horst, M. (2009). Umstellung einer FPGA-basierten Implementation des 2D-Warping-Algorithmus auf Min-Warping. Bachelor thesis, Bielefeld University, Faculty of Technology, Computer Engineering Group.

  • Hübner, W. (2005). From homing behavior to cognitive mapping: Integration of egocentric pose relations and allocentric landmark information in a graph model. Ph.D. thesis, Fachbereich 3 (Mathematik & Informatik), Universität Bremen.

  • Hübner, W., & Mallot, H. A. (2002). Integration of metric place relations in a landmark graph. In J. R. Dorronsoro (Ed.), Lecture notes in computer science : Vol. 2415. Proc. int. conf. on artificial neural networks (ICANN) (pp. 825–830). Berlin: Springer.

    Google Scholar 

  • Hübner, W., & Mallot, H. A. (2007). Metric embedding of view-graphs—a vision and odometry-based approach to cognitive mapping. Autonomous Robots, 23(3), 183–196.

    Article  Google Scholar 

  • Ishiguro, H., & Tsuji, S. (1996). Image-based memory of environment. In Proc. int. conf. on intelligent robots and systems IROS’96 (Vol. 2, pp. 634–639).

  • Jogan, M., & Leonardis, A. (1999). Panoramic eigenimages for localisation. In Lecture notes in computer science : Vol. 1689. Computer analysis of images and patterns (pp. 558–567). Berlin: Springer.

    Chapter  Google Scholar 

  • Jogan, M., & Leonardis, A. (2003). Robust localization using an omnidirectional appearance-based subspace model of environment. Robotics and Autonomous Systems, 45(1), 51–72.

    Article  Google Scholar 

  • Kollmeier, T., Röben, F., Schenck, W., & Möller, R. (2007). Spectral contrasts for landmark navigation. Journal of the Optical Society of America A, 24(1), 1–10.

    Article  Google Scholar 

  • Kortenkamp, D. (1993). Cognitive maps for mobile robots: A representation for mapping and navigation. Ph.D. thesis, The University of Michigan.

  • Kortenkamp, D., & Weymouth, T. (1994). Topological mapping for mobile robots using a combination of sonar and vision sensing. In Proc. twelfth national conf. on artificial intelligence (AAAI-94).

  • Kreft, S. (2007). Reinigungstrajektorien mobiler Roboter unter visueller Steuerung. Diploma thesis, Bielefeld University, Faculty of Technology, Computer Engineering Group.

  • Krzykawski, M. (2008). Implementierung eines 2D-Warping-Verfahrens auf einem FPGA. Diploma thesis, Bielefeld University, Faculty of Technology, Computer Engineering Group.

  • Kuipers, B. (1998). A hierarchy of qualitative representations for space. In C. Freksa, C. Habel, & K. F. Wender (Eds.), Lecture notes in computer science : Vol. 1404. Spatial cognition—an interdisciplinary approach to representing and processing spatial knowledge (pp. 337–350). Berlin: Springer.

    Google Scholar 

  • Kuipers, B. (2000). The spatial semantic hierarchy. Artificial Intelligence, 119(1–2), 191–233.

    Article  MATH  MathSciNet  Google Scholar 

  • Kuipers, B. J., & Byun, Y.-T. (1988). A robust, qualitative approach to a spatial learning mobile robot. In SPIE sensor fusion: spatial reaoning and scene interpretation 1003.

  • Kuipers, B., & Byun, Y.-T. (1991). A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations. Robotics and Autonomous Systems, 8(1–2), 47–63.

    Article  Google Scholar 

  • Labrosse, F. (2004). Visual compass. In Proc. towards autonomous robotic systems. Colchester: University of Essex.

    Google Scholar 

  • Labrosse, F. (2006). The visual compass: Performance and limitations of an appearance-based method. Journal of Field Robotics, 23(10), 913–941.

    Article  Google Scholar 

  • Labrosse, F. (2007). Short and long-range visual navigation using warped panoramic images. Robotics and Autonomous Systems, 55(9), 675–684.

    Article  Google Scholar 

  • Lambrinos, D. (1999). Navigation in biorobotic agents. Ph.D. thesis, Department of Computer Science, University of Zurich, Switzerland.

  • Lambrinos, D., Möller, R., Labhart, T., Pfeifer, R., & Wehner, R. (2000). A mobile robot employing insect strategies for navigation. Robotics and Autonomous Systems, special issue: Biomimetic Robots, 30(1–2), 39–64.

    Google Scholar 

  • Levitt, T. S., & Lawton, D. T. (1990). Qualitative navigation for mobile robots. Artificial Intelligence, 44(3), 305–360.

    Article  Google Scholar 

  • Li, J., & Allinson, N. M. (2008). A comprehensive review of current local features for computer vision. Neurocomputing, 71(10–12), 1771–1787.

    Article  Google Scholar 

  • Lim, J., & Barnes, N. (2009). Robust visual homing with landmark angles. In Proc. robotics: science and systems. Seattle, USA.

  • Linåker, F., & Ishikawa, H. (2006). Real-time appearance-based Monte Carlo localization. Robotics and Autonomous Systems, 54(3), 205–220.

    Article  Google Scholar 

  • Liu, M., Pradalier, C., Chen, Q., & Siegwart, R. (2010). A bearing-only 2D/3D-homing method under a visual servoing framework. In Proc. IEEE int. conf. on robotics and automation (pp. 4062–4067).

  • Loizou, S. G., & Kumar, V. (2007). Biologically inspired bearing-only navigation and tracking. In Proc. 46th IEEE conf. on decision and control (pp. 1386–1391).

  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.

    Article  Google Scholar 

  • Mahon, I., Williams, S. B., Pizarro, O., & Johnson-Roberson, M. (2008). Efficient view-based SLAM using visual loop closures. IEEE Transactions on Robotics, 24(5), 1002–1014.

    Article  Google Scholar 

  • Mallot, H. A. (1999). Spatial cognition: Behavioral competences, neural mechanisms, and evolutionary scaling. Kognitionswissenschaft, 8(1), 40–48.

    Article  Google Scholar 

  • Mangan, M., & Webb, B. (2009). Modelling place memory in crickets. Biological Cybernetics, 101(4), 307–323.

    Article  MathSciNet  Google Scholar 

  • Menegatti, E., Maeda, T., & Ishiguro, H. (2004). Image-based memory for robot navigation using properties of omnidirectional images. Robotics and Autonomous Systems, 47(4), 251–267.

    Article  Google Scholar 

  • Meyer, J.-A., & Filliat, D. (2003). Map-based navigation in mobile robots. II. A review of map-learning and path-planning strategies. Cognitive Systems Research, 4(4), 283–317.

    Article  Google Scholar 

  • Möller, R. (2000). Insect visual homing strategies in a robot with analog processing. Biological Cybernetics, 83(3), 231–243.

    Article  MATH  Google Scholar 

  • Möller, R. (2001). Do insects use templates or parameters for landmark navigation? Journal of Theoretical Biology, 210(1), 33–45.

    Article  Google Scholar 

  • Möller, R. (2002a). A biorobotics approach to the study of insect visual homing strategies. Habilitationsschrift, Wirtschaftswissenschaftliche Fakultät der Universität Zürich.

  • Möller, R. (2002b). Insects could exploit UV-green contrast for landmark navigation. Journal of Theoretical Biology, 214(4), 619–631.

    Article  Google Scholar 

  • Möller, R. (2009). Local visual homing by warping of two-dimensional images. Robotics and Autonomous Systems, 57(1), 87–101.

    Article  Google Scholar 

  • Möller, R., & Vardy, A. (2006). Local visual homing by matched-filter descent in image distances. Biological Cybernetics, 95(5), 413–430.

    Article  MathSciNet  Google Scholar 

  • Möller, R., Maris, M., & Lambrinos, D. (1999). A neural model of landmark navigation in insects. Neurocomputing, 26–27, 801–808.

    Article  Google Scholar 

  • Möller, R., Lambrinos, D., Roggendorf, T., Pfeifer, R., & Wehner, R. (2001). Insect strategies of visual homing in mobile robots. In B. Webb & T. R. Consi (Eds.), Biorobotics. Methods and applications (pp. 37–66). Cambridge: AAAI Press, MIT Press.

    Google Scholar 

  • Möller, R., Vardy, A., Kreft, S., & Ruwisch, S. (2007). Visual homing in environments with anisotropic landmark distribution. Autonomous Robots, 23(3), 231–245.

    Article  Google Scholar 

  • Nelson, R., & Aloimonos, A. (1988). Finding motion parameters from spherical motion fields (or the advantage of having eyes in the back of your head). Biological Cybernetics, 58(4), 261–273.

    Article  Google Scholar 

  • Nelson, R. C. (1989). Visual homing using associative memory. In Proc. image understanding workshop (pp. 245–262). Palo Alto: Morgan Kaufmann.

    Google Scholar 

  • Newman, P., Sibley, G., Smith, M. et al. (2009). Navigating, recognizing and describing urban spaces with vision and lasers. International Journal of Robotics Research, 28(11–12), 1406–1433.

    Google Scholar 

  • Pajdla, T., & Hlaváč, V. (1999). Zero phase representations of panoramic image for image based localization. In F. Solina & A. Leonardis (Eds.), Lecture notes in computer science : Vol. 1689. Proc. 8th int. conf. on computer analysis of images and patterns (pp. 550–557). Berlin: Springer.

    Chapter  Google Scholar 

  • Pinette, B. (1991). Qualitative homing. In Proc. IEEE int. symposium on intelligent control (pp. 318–323).

  • Prescott, T. J. (1996). Spatial representation for navigation in animats. Adaptive Behavior, 4(2), 85–123.

    Article  Google Scholar 

  • Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (2002). Numerical recipes in C (2nd ed.). Cambridge: Cambridge University Press.

    Google Scholar 

  • Remolina, E., & Kuipers, B. (2004). Towards a general theory of topological maps. Artificial Intelligence, 152(1), 47–104.

    Article  MATH  MathSciNet  Google Scholar 

  • Rizzi, A., Duina, D., Inelli, S., & Cassinis, R. (2001). A novel visual landmark matching for a biologically inspired homing. Pattern Recognition Letters, 22(13), 1371–1378.

    Article  MATH  Google Scholar 

  • Röfer, T. (1997). Controlling a wheelchair with image-based homing. In Proc. AISB workshop on spatial reasoning in mobile robots and animals. No. UMCS-97-4-1 in Technical Report Series. Manchester University, Department of Computer Science.

  • Saez Pons, J., Hübner, W., Dahmen, H., & Mallot, H. A. (2007). Vision-based robotic homing in dynamic environments. In Proc. 13th IASTED int. conf. on robotics and applications (pp. 293–298).

  • Schatz, A. (2006). Visuelle Navigation mit “Scale Invariant Feature Transform”. Diploma thesis, Bielefeld University, Faculty of Technology, Computer Engineering Group.

  • Se, S., Lowe, D., & Little, J. (2002). Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. International Journal of Robotics Research, 21(8), 735–758.

    Article  Google Scholar 

  • Se, S., Lowe, D. G., & Little, J. J. (2005). Vision-based global localization and mapping for mobile robots. IEEE Transactions on Robotics, 21(3), 364–375.

    Article  Google Scholar 

  • Siciliano, B., & Khatib, O. (Eds.) (2008). Springer handbook of robotics. Berlin: Springer.

    MATH  Google Scholar 

  • Smith, L., Philippides, A., Graham, P., Baddeley, B., & Husbands, P. (2007). Linked local navigation for visual route guidance. Adaptive Behavior, 15(3), 257–271.

    Article  Google Scholar 

  • Stachniss, C. (2009). Robotic mapping and exploration. Berlin: Springer.

    Book  Google Scholar 

  • Steder, B., Grisetti, G., Stachniss, C., & Burgard, W. (2008). Visual SLAM for flying vehicles. IEEE Transactions on Robotics, 24(5), 1088–1093.

    Article  Google Scholar 

  • Stürzl, W. (2004). Sensorik und Bildverarbeitung für Landmarken-basierte Navigation. Ph.D. thesis, University of Tubingen.

  • Stürzl, W., & Mallot, H. A. (2002). Vision-based homing with a panoramic stereo sensor. In Lecture notes in computer science : Vol. 2525. Biologically motivated computer vision (pp. 620–628). Berlin: Springer.

    Chapter  Google Scholar 

  • Stürzl, W., & Mallot, H. A. (2006). Efficient visual homing based on Fourier transformed panoramic images. Robotics and Autonomous Systems, 54(4), 300–313.

    Article  Google Scholar 

  • Stürzl, W., & Möller, R. (2007). An insect-inspired active vision approach for orientation estimation with panoramic images. In Lecture notes in computer science : Vol. 4527. Bio-inspired modeling of cognitive tasks (Proc. IWINAC 07) (pp. 61–70). Berlin: Springer.

    Chapter  Google Scholar 

  • Stürzl, W., & Zeil, J. (2007). Depth, contrast and view-based homing in outdoor scenes. Biological Cybernetics, 96(5), 519–531.

    Article  MATH  Google Scholar 

  • Taylor, C. J., & Kriegman, D. J. (1998). Vision-based motion planning and exploration algorithms for mobile robots. IEEE Transactions on Robotics and Automation, 14(3), 417–426.

    Article  Google Scholar 

  • Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. Cambridge: MIT Press.

    MATH  Google Scholar 

  • Todt, E., & Torras, C. (2004). Detecting salient cues through illumination-invariant color ratios. Robotics and Autonomous Systems, 48(2–3), 111–130.

    Article  Google Scholar 

  • Trullier, O., Wiener, S. I., Berthoz, A., & Meyer, J.-A. (1997). Biologically based artificial navigation systems: Review and prospects. Progress in Neurobiology, 51(5), 483–544.

    Article  Google Scholar 

  • Ulrich, I., & Nourbakhsh, I. (2000). Appearance-based place recognition for topological localization. In Proc. IEEE intl. conf. on robotics and automation ICRA’00 (Vol. 2, pp. 1023–1029).

  • Vardy, A. (2005). Biologically plausible methods for robot visual homing. Ph.D. thesis, Carleton University, Canada.

  • Vardy, A. (2006). Long-range visual homing. In Proc. IEEE intl. conf. on robotics and biomimetics ROBIO’06 (pp. 220–226).

  • Vardy, A., & Möller, R. (2005). Biologically plausible visual homing methods based on optical flow techniques. Connection Science, 17(1–2), 47–89.

    Article  Google Scholar 

  • Vardy, A., & Oppacher, F. (2003). Low-level visual homing. In W. Banzhaf, T. Christaller, P. Dittrich, J. T. Kim, & J. Ziegler (Eds.), Lecture notes in artificial intelligence : Vol. 2801. Advances in artificial life—proc. 7th European conf. on artificial life (ECAL) (pp. 875–884). Berlin: Springer.

    Google Scholar 

  • Vardy, A., & Oppacher, F. (2004). Anatomy and physiology of an artificial vision matrix. In A. Ijspreet, M. Murata, & N. Wakamiya (Eds.), Lecture notes in computer science : Vol. 3141. Biologically inspired approaches to advanced information technology: first int. workshop, BioADIT 2004 (pp. 290–305). Berlin: Springer.

    Chapter  Google Scholar 

  • Vardy, A., & Oppacher, F. (2005). A scale invariant local image descriptor for visual homing. In S. Wermter, G. Palm, & E. Elshaw (Eds.), Lecture notes in computer science : Vol. 3575. Biomimetic neural learning for intelligent robots (pp. 362–381). Berlin: Springer.

    Chapter  Google Scholar 

  • Venjakob, D. (2007). Verbesserung des Blockmatching-Verfahrens durch Feature-Transformationen. Bachelor thesis, Bielefeld University, Faculty of Technology, Computer Engineering Group.

  • Webb, B. (2001). Can robots make good models of biological behaviour? Behavioral and Brain Sciences, 24(6), 1033–1050.

    Google Scholar 

  • Weber, K., Venkatesh, S., & Srinivasan, M. (1999). Insect-inspired robotic homing. Adaptive Behavior, 7(1), 65–97.

    Article  Google Scholar 

  • Wei, R., Austin, D., & Mahony, R. (2005). Biomimetic application of desert ant visual navigation for mobile robot docking with weighted landmarks. International Journal of Intelligent Systems Technologies and Applications, 1(1/2), 174–190.

    Article  Google Scholar 

  • Wolfart, E., Fisher, R. B., & Walker, A. (1995). Position refinement for a navigating robot using motion information based on honey bee strategies. In Proc. symp. intelligent robotic systems (SIRS95) (pp. 257–264).

  • Zampoglou, M., Szenher, M., & Webb, B. (2006). Adaptation of controllers for image-based homing. Adaptive Behavior, 14(4), 381–399.

    Article  Google Scholar 

  • Zeil, J., Boeddecker, N., & Stürzl, W. (2009). Visual homing in insects and robots. In D. Floreano, J.-C. Zufferey, M. V. Srinivasan, & C. Ellington (Eds.), Flying insects and robots (pp. 87–100). Berlin: Springer. Chap. 7.

    Chapter  Google Scholar 

  • Zeil, J., Hoffmann, M. I., & Chahl, J. S. (2003). Catchment areas of panoramic images in outdoor scenes. Journal of the Optical Society of America A, 20(3), 450–469.

    Article  Google Scholar 

  • Zhang, A. M., & Kleeman, L. (2009). Robust appearance based visual route following for navigation in large-scale outdoor environments. International Journal of Robotics Research, 28(3), 331–356.

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Engineering, Faculty of Technology, and Center of Excellence ‘Cognitive Interaction Technology’, Bielefeld University, 33594, Bielefeld, Germany

    Ralf Möller, Martin Krzykawski & Lorenz Gerstmayr

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  1. Ralf Möller
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  2. Martin Krzykawski
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  3. Lorenz Gerstmayr
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Correspondence to Ralf Möller.

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Möller, R., Krzykawski, M. & Gerstmayr, L. Three 2D-warping schemes for visual robot navigation. Auton Robot 29, 253–291 (2010). https://doi.org/10.1007/s10514-010-9195-y

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