In this paper, we propose a robust and fast active contour based method to free space detection in omnidirectional images where the problem of falsely detected obstacles is solved. We define a new functional energy formulation including altitude estimation of keypoints extracted nearby the active contour modeling the free space. The free space, so extracted, could help the robot in real time navigation and environment exploration tasks. To validate the efficiency of the proposed approach, the paper shows comparative results achieved with a classical formulation and our formulation of active contour energies, using images acquired by a robot exploring unknown indoor and outdoor environments, with no prior knowledge of the shape or the extend of the free space.

This paper contributes to adapt parametric and geometric active contour methods in a new framework to handle real time free space extraction while taking advantage of the properties of omnivision. Both methods were formally and algorithmically adapted and improved. Some comparative results, achieved on unknown indoor and outdoor images, are presented to validate the efficiency of our two snake based approaches. We also show that active contours can be applied to make a robot navigate autonomously, only using real omni images, thanks to the extracted free space skeleton.

Omnidirectional catadioptric cameras offer a large field of view and a complete information about the world surrounding the robot. In this paper, we describe a method to perform a fast and robust extraction of the omnidirectional free space using active contour models. The extracted free space could help the robot in real time navigation and environment exploration tasks. The presented approach will be compared to classical active contour methods usually applied to object segmentation. Some comparative results achieved in indoor and outdoor environments are shown to validate the approach.