Research of Binocular Visual Inertial Algorithm Based on Point and Line Features

Abstract

To solve the problem of poor performance of the binocular visual inertial odometer VINS-Fusion in scenes with low texture and large luminosity changes, a binocular visual inertial odometer PLVINS-Fusion is designed that integrates line feature measurement information, which use line features to easy to extract in low-texture scenes, and have the advantage of more robust tracking performance in scenes with large luminosity changes. Point and line features are extracted in the front-end visual extraction at the same time, and line feature residuals are added to the back-end nonlinear optimization, construct a bag-of-words model combining point and line features in the loop detection module. On this basis, a real-time photometric calibration algorithm is adopted to jointly optimize the exposure time, the camera response function and the vignetting factor, and the stability of KLT optical flow tracking is improved by correcting the image brightness. Experiments on benchmark dataset show that the optimized algorithm has higher robustness and effectively improves the positioning accuracy, and meets the real-time performance requirement.