Monocular Navigation in Large Scale Dynamic Environments
This addresses navigation challenges for autonomous systems in complex road scenarios, representing an incremental improvement over existing methods.
The paper tackles the problem of robust motion reconstruction in large-scale dynamic environments using monocular vision, achieving separation of direction and magnitude for dynamic object states where conventional binocular and structure-from-motion methods fail due to small disparity signals and unobserved agent motion.
We present a processing technique for a robust reconstruction of motion properties for single points in large scale, dynamic environments. We assume that the acquisition camera is moving and that there are other independently moving agents in a large environment, like road scenarios. The separation of direction and magnitude of the reconstructed motion allows for robust reconstruction of the dynamic state of the objects in situations, where conventional binocular systems fail due to a small signal (disparity) from the images due to a constant detection error, and where structure from motion approaches fail due to unobserved motion of other agents between the camera frames. We present the mathematical framework and the sensitivity analysis for the resulting system.