Descriptor-Length Reduction Using Low-Variance Filter for Visual Odometry

TLDR: The proposed scheme of variance-based descriptor length reduction is found to reduce the overall time taken by the motion estimation framework while estimating the transformation with similar accuracy as that with full-length feature vector.

Abstract: Visual odometry is a popular technique used to estimate motion in GPS-challenged environment, whose accuracy depends on the features extracted from the images. In past attempts to improved feature distinctiveness, these features have become complex and lengthier, requiring more storage space and computational power for matching. In this paper, an attempt is made toward reducing the length of these feature descriptors while maintaining a similar accuracy in pose estimation. Elimination of feature indices based on variance analysis on feature column sets is proposed and experimented in this paper. The features with reduced descriptor length are applied over the 3D-2D visual odometry pipeline and experimented on KITTI dataset for evaluating its efficacy. The proposed scheme of variance-based descriptor length reduction is found to reduce the overall time taken by the motion estimation framework while estimating the transformation with similar accuracy as that with full-length feature vector.