Featureless Motion Vector-Based Simultaneous Localization, Planar Surface Extraction, and Moving Obstacle Tracking
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abstract
Springer International Publishing Switzerland 2015. Motion vectors (MVs) characterize the movement of pixel blocks in video streams and are readily available. MVs not only allow us to avoid expensive feature transform and correspondence computations but also provide themotion information for both the environment and moving obstacles. This enables us to develop a new framework that is capable of simultaneous localization, scene mapping, and moving obstacle tracking. This method first extracts planes from MVs and their corresponding pixel macro blocks (MBs) using properties of plane-induced homographies. We then classify MBs as stationary or moving using geometric constraints on MVs. Planes are labeled as part of the stationary scene or moving obstacles using MB voting. Therefore, we can establish planes as observations for extended Kalman filters (EKFs) for both the stationary scene and moving objects. We have implemented the proposed method. The results show that the proposed method can establish planebased rectilinear scene structure and detect moving objects while achieving similar localization accuracy of 1-Point EKF. More specifically, the system detects moving obstacles at a true positive rate of 96.6% with a relative absolution trajectory error of no more than 2.53%.
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Algorithmic Foundations of Robotics XI - Selected Contributions of the Eleventh International Workshop on the Algorithmic Foundations of Robotics, WAFR 2014, 3-5 August 2014, Boazii University, stanbul, Turkey
