We propose an approach for vehicle localization in dense urban
environments using a stereoscopic system and a GPS sensor.
Stereoscopic system is used to capture the stereo video flow, to
recover the environments, and to estimate the vehicle motion based
on feature detection, matching, and triangulation from every image
pair. A relative depth constraint is applied to eliminate the
tracking couples which are inconsistent with the vehicle
ego-motion. Then the optimal rotation and translation between the
current and the reference frames are computed using an RANSAC
based minimization method. Meanwhile, GPS positions are obtained
by an on-board GPS receiver and periodically used to adjust the
vehicle orientations and positions estimated by stereovision. The
proposed method is tested with two real sequences obtained by a
GEM vehicle equipped with a stereoscopic system and a RTK-GPS
receiver. The results show that the vision/GPS integrated
trajectory can fit the ground truth better than the vision-only
method, especially for the vehicle orientation. And vice-versa,
the stereovision-based motion estimation method can correct the
GPS signal failures (e.g., GPS jumps) due to multipath problem or
other noises.
Robust vehicle localization in urban environments using probabilistic maps