We propose to use an active shape model for correcting a road map stably. Active shape model can deform itself preserving a given basic shape by restricting the deformation to an affine transformation. In order to consider a topological connections for road map, we apply the deformation to @ not single road but simultaneously several roads, one step at a time. By iterating the deformation step, road network is refined gradually. Finally, experimental results will show that proposed method can refine the existing road map correctly by fitting aerial image.
This paper presents a novel framework of vision-based road navigation system, which superimposes virtual 3D navigation indicators and traffic signs onto the real road scene in an Augmented Reality (AR) space. To properly align objects in the real and virtual world, it is essential to keep tracking camera's exact 3D position and orientation, which is well known as the Registration Problem. Traditional vision based or inertial sensor based solutions are mostly designed for well-structured environment, which is however unavailable for outdoor uncontrolled road navigation applications. This paper proposed a hybrid system that combines vision, GPS and 3D inertial gyroscope technologies to stabilize the camera pose estimation output. The fusion approach is based on our PMM (parameterized model matching) algorithm, in which the road shape model is derived from the digital map referring to GPS absolute road position, and matches with road features extracted from the real image. Inertial data estimates the initial possible motion, and also serves as relative tolerance to stable the pose output. The algorithms proposed in this paper are validated with the experimental results of real road tests under different road conditions.