Harris, SIFT and SURF features comparison for vehicle localization based on virtual 3D model and camera

This paper proposed a new vehicle geo-localization method in urban environment integrating a new source of information that is a virtual 3D city model. This 3D model provides a realistic representation of the navigation environment of the vehicle. To optimize the performance of vehicle geo-localization system, several sources of information are integrated for their complementarity and redundancy: a GPS receiver, proprioceptive sensors (odometers and gyrometer), a video camera and a virtual 3D city model. The pose estimation algorithm used to fuse the different sensors data is an IMM-UKF (Interacting Multiple Model - Unscented Kalman Filter). The proprioceptive sensors allow to continuously estimating the dead-reckoning position and orientation of the vehicle. This dead-reckoning estimation of the pose is corrected by GPS measurements. Moreover, a 3D model/camera based observation of the vehicle pose is constructed to compensate the drift of the dead-reckoning localization when GPS measurements are unavailable for a long time. This pose observation is based on the matching between the virtual image extracted from the 3D city model and the real image acquired by the camera. The observation construction is composed of two major parts. The first part consists in detecting and matching the feature points of the real and virtual images. Three features are compared: Harris corner, SIFT (Scale Invariant Feature Transform) and SURF (Speed Up Robust Features). The second part is the pose computation using POSIT algorithm and the previously matched features set. The developed approach has been tested on a real sequence and the obtained results proved the feasibility and robustness of the approach.