Optic Flow and Autonomous Navigation

Many animals, especially insects, compute and use optic flow to control their motion direction and to avoid obstacles. Recent advances in computer vision have shown that an adequate optic flow can be computed from image sequences. Therefore studying whether artificial systems, such as robots, can use optic flow for similar purposes is of particular interest. Experiments are reviewed that suggest the possible use of optic flow for the navigation of a robot moving in indoor and outdoor environments. The optic flow is used to detect and localise obstacles in indoor scenes, such as corridors, offices, and laboratories. These routines are based on the computation of a reduced optic flow. The robot is usually able to avoid large obstacles such as a chair or a person. The avoidance performances of the proposed algorithm critically depend on the optomotor reaction of the robot. The optic flow can be used to understand the ego-motion in outdoor scenes, that is, to obtain information on the absolute velocity of the moving vehicle and to detect the presence of other moving objects. A critical step is the correction of the optic flow for shocks and vibrations present during image acquisition. The results obtained suggest that optic flow can be successfully used by biological and artifical systems to control their navigation. Moreover, both systems require fast and accurate optomotor reactions and need to compensate for the instability of the viewed world.

[1]  Patrick Bouthemy,et al.  Motion segmentation and qualitative dynamic scene analysis from an image sequence , 1993, International Journal of Computer Vision.

[2]  David J. Heeger,et al.  Optical flow using spatiotemporal filters , 2004, International Journal of Computer Vision.

[3]  Andrea Giachetti,et al.  The use of optical flow for the autonomous navigation , 1994, ECCV.

[4]  Hans-Hellmut Nagel,et al.  Optical Flow Estimation: Advances and Comparisons , 1994, ECCV.

[5]  Patrick Bouthemy,et al.  Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Vincent Torre,et al.  The Accuracy of the Computation of Optical Flow and of the Recovery of Motion Parameters , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  N. Franceschini,et al.  From insect vision to robot vision , 1992 .

[8]  A. Verri,et al.  Computational aspects of motion perception in natural and artificial vision systems. , 1992, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[9]  A. Verri,et al.  First-order analysis of optical flow in monkey brain. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[10]  C..  Vision-based Vehicle Guidance , 1992, Springer Series in Perception Engineering.

[11]  R. Wurtz,et al.  Sensitivity of MST neurons to optic flow stimuli. II. Mechanisms of response selectivity revealed by small-field stimuli. , 1991, Journal of neurophysiology.

[12]  R. Wurtz,et al.  Sensitivity of MST neurons to optic flow stimuli. I. A continuum of response selectivity to large-field stimuli. , 1991, Journal of neurophysiology.

[13]  Hugo Maes,et al.  Position invariance of optic flow component selectivity differentiates monkey mst and fst cells from mt cells , 1991 .

[14]  Alessandro Verri,et al.  Computing optical flow from an overconstrained system of linear algebraic equations , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[15]  Leslie G. Ungerleider,et al.  Pathways for motion analysis: Cortical connections of the medial superior temporal and fundus of the superior temporal visual areas in the macaque , 1990, The Journal of comparative neurology.

[16]  A. Verri,et al.  Differential techniques for optical flow , 1990 .

[17]  R A Andersen,et al.  Hierarchical processing of motion in the visual cortex of monkey. , 1990, Cold Spring Harbor symposia on quantitative biology.

[18]  K. Tanaka,et al.  Analysis of motion of the visual field by direction, expansion/contraction, and rotation cells clustered in the dorsal part of the medial superior temporal area of the macaque monkey. , 1989, Journal of neurophysiology.

[19]  A. Verri,et al.  Mathematical properties of the two-dimensional motion field: from singular points to motion parameters , 1989 .

[20]  D. C. Van Essen,et al.  Concurrent processing streams in monkey visual cortex , 1988, Trends in Neurosciences.

[21]  S. Shipp,et al.  The functional logic of cortical connections , 1988, Nature.

[22]  D. Hubel,et al.  Segregation of form, color, movement, and depth: anatomy, physiology, and perception. , 1988, Science.

[23]  田中 啓治.  Analysis of Local and Wide-Field Movements in the Superior Temporal Visual Areas of the Macaque Monkey , 1987 .

[24]  Alessandro Verri,et al.  Against Quantitative Optical Flow , 1987 .

[25]  Leslie G. Ungerleider,et al.  Cortical connections of visual area MT in the macaque , 1986, The Journal of comparative neurology.

[26]  Keiji Tanaka,et al.  Integration of direction signals of image motion in the superior temporal sulcus of the macaque monkey , 1986, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[27]  K. Tanaka,et al.  Analysis of local and wide-field movements in the superior temporal visual areas of the macaque monkey , 1986, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[28]  Tomaso A. Poggio,et al.  On Edge Detection , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  E H Adelson,et al.  Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[30]  E. Adelson,et al.  The analysis of moving visual patterns , 1985 .

[31]  E. Hildreth.  The computation of the velocity field , 1984, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[32]  David N. Lee,et al.  Visual Timing in Hitting An Accelerating Ball , 1983, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[33]  D C Van Essen,et al.  Functional properties of neurons in middle temporal visual area of the macaque monkey. I. Selectivity for stimulus direction, speed, and orientation. , 1983, Journal of neurophysiology.

[34]  Hans-Hellmut Nagel,et al.  Displacement vectors derived from second-order intensity variations in image sequences , 1983, Comput. Vis. Graph. Image Process..

[35]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.