  In this paper, we propose a simple way to utilize stereo camera data to
improve feature descriptors. Computer vision algorithms that use a stereo
camera require some calculations of 3D information. We leverage this
pre-calculated information to improve feature descriptor algorithms. We use the
3D feature information to estimate the scale of each feature. This way, each
feature descriptor will be more robust to scale change without significant
computations. In addition, we use stereo images to construct the descriptor
vector. The Scale-Invariant Feature Transform (SIFT) and Fast Retina Keypoint
(FREAK) descriptors are used to evaluate the proposed method. The scale
normalization technique in feature tracking test improves the standard SIFT by
8.75% and improves the standard FREAK by 28.65%. Using the proposed stereo
feature descriptor, a visual odometry algorithm is designed and tested on the
KITTI dataset. The stereo FREAK descriptor raises the number of inlier matches
by 19% and consequently improves the accuracy of visual odometry by 23%.
