Toward featureless visual navigation: Simultaneous localization and planar surface extraction using motion vectors in video streams

Unlike the traditional feature-based methods, we propose using motion vectors (MVs) from video streams as inputs for visual navigation. Although MVs are very noisy and with low spatial resolution, MVs do possess high temporal resolution which means it is possible to merge MVs from different frames to improve signal quality. Homography filtering and MV thresholding are proposed to further improve MV quality so that we can establish plane observations from MVs. We propose an extended Kalman filter (EKF) based approach to simultaneously track robot motion and planes. We formally model error propagation of MVs and derive variance of the merged MVs. We have implemented the proposed method and tested it in physical experiments. Results show that the system is capable of performing robot localization and plane mapping with a relative trajectory error of less than 5.1%.