Omnidirectional Optical Flow and Visual Motion Detection for Autonomous Robot Navigation

Autonomous robot navigation in dynamic environments requires robust detection of egomotion and independent motion. This thesis introduces a novel solution to the problem of visual independent motion detection by interpreting the topological features of omnidirectional dense optical flow field and determining the background - egomotion direction. The thesis solves the problem of visual independent motion detection in four interdependent subtasks. Independent Motion Detection can only be accomplished if the egomotion detection yields a relevant background motion model. Therefore, the problem of Egomotion Detection is solved first by exploiting the topological structures of the global omnidirectional optical flow fields. The estimation of the optical flow field is the prerequisite of the Egomotion-Detection task. Since the omnidirectional projection introduces non-affine deformations on the image plane, the known optical flow calculation methods have to be modified to yield accurate results. This modification is introduced here as another subtask, Omnidirectional Optical Flow Estimation. The experiments concerning the 3D omnidirectional scene capturing are grouped under the fourth subtask 3D Omni-Image Processing