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.
This paper presents a dynamical solution of the registration problem for on-road navigation applications via 3D-2D parameterized model matching algorithm. Traditional camera’s three dimensional (3D) position and pose estimation algorithms always employ the fixed and known-structure models as well as the depth information to obtain the 3D-2D correlations, which is however unavailable for on-road navigation applications since there are no fixed models in the general road scene. With the constraints of road structure and on-road navigation features, this paper presents a 2D digital road map based road shape modeling algorithm. Dynamically generated multi-lane road shape models are used to match real road scene to estimate camera 3D position and pose data. Our algorithms successfully simplified the 3D-2D correlation problem to the 2D-2D road model matching on the projective image. The algorithms proposed in this paper are validated with the experimental results from real road test under different conditions and types of road.