PnP-epipolar optimization approach for VSLAM using robust pose prediction

Abstract Visual Simultaneous Localization and Mapping (VSLAM) is easily influenced by aggressive motion and low overlap images. Based on the challenge, we propose a VSLAM system with the robust tracking method. The feature tracking model is constructed by the robust pose prediction and PnP-Epipolar optimization. Firstly, as conventional VSLAM systems struggle with aggressive motion, we improve the constant acceleration prediction to estimate a dependable initial pose. The pose prediction takes into account the variation of the rotation axis, providing a more reliable initial guess for the pose optimization. Secondly, a novel global feature matching with BoW acceleration and map-point projection is performed to increase the number of matches. The matching method considers the points without map-point observation. Thirdly, according to the matches, we constitute the PnP-Epipolar constraint to optimize the pose with low overlap images. Finally, we compare our algorithm with several state-of-the-art VSLAM systems on KITTI dataset and EuRoC MAV dataset. The experimental results indicate that the proposed VSLAM reduces the RMSE by about 30% on average. For some sequences containing aggressive angle turning scene, the RMSE reduction even reaches 50%.