Highly Efficient Line Segment Tracking with an IMU-KLT Prediction and a Convex Geometric Distance Minimization

Line segment features become popular in SLAM community. Usually, line-based SLAM systems utilize local appearance descriptors for line segment tracking. However, traditional descriptor-based line segment tracking algorithms suffer from the problem that accuracy and speed cannot be possessed simultaneously, which affects the performance of line-based SLAM systems negatively. We propose a novel line segment tracking method with an IMU-KLT line segment prediction and a convex geometric distance minimization to boost line segment tracking performance in both accuracy and speed. Particularly, the proposed convex geometric distance minimization uses a l1-norm model to minimize geometric constraints between predicted line segments and extracted line segments efficiently. Furthermore, the line segment tracking is embedded into a VIO system and we adapt it to obtain more reliable point tracking. Experimental results on public datasets show that the proposed line segment tracking method achieves much higher accuracy and much less time cost than state-of-the-art level, where not only the number of correct matches increases but also the inlier ratios are increased by at least 35.1% along with a 3 times faster speed. Besides, the VIO system combining the proposed line segment tracking is improved in terms of accuracy.