Robust Stereo Feature Descriptor for Visual Odometry

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 SIFT and 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%. We also use a simple visual odometry algorithm and test it on the KITTI datasets. The stereo FREAK descriptor raises the number of inlier matches by 19% and consequently improves the accuracy of visual odometry by 23%.