Visual Odometry With Point and Line Features Based on Underground Tunnel Environment

Abstract

Compared with the interior environment, there exists a lot of noise and dust in an underground tunnel, and the light is unstable, which is difficult to track by the direct method. Moreover, errors in line segment projection and line feature drift under the influence of light can lead to significant deviations in the odometry. Consequently, to improve the accuracy and robustness of visual odometry, a point-line feature stereo visual odometry system is proposed in this paper. The system combines ORB features and LSD line features; using the angle relationship of line projection, we propose a new method for calculating the reprojection error of line features, reconstruct the reprojection model based on line features, and construct a new reprojection error model based on point-line features, which adds an angle constraint to the reprojection of line features and solves the instability caused by line projection error. It is shown in our experiments on the KITTI, New College dataset, that the translation error of our system is reduced by about 40% on average compared to PLVO, with a reduction in relative positioning error. Experiments in the hallway and underground tunnel environments have shown that the maximum positioning error of our system has been reduced by 75% in hallways and by 56.7% in an underground tunnel. Therefore, our algorithm effectively improves the localization accuracy and is more advantageous in low-texture environments.