KLT-VIO: Real-time Monocular Visual-Inertial Odometry

Abstract

This paper proposes a Visual-Inertial Odometry (VIO) algorithm that relies solely on monocular cameras and Inertial Measurement Units (IMU), capable of real-time self-position estimation for robots during movement. By integrating the optical flow method, the algorithm tracks both point and line features in images simultaneously, significantly reducing computational complexity and the matching time for line feature descriptors. Additionally, this paper advances the triangulation method for line features, using depth information from line segment endpoints to determine their Plücker coordinates in three-dimensional space. Tests on the EuRoC datasets show that the proposed algorithm outperforms PL-VIO in terms of processing speed per frame, with an approximate 5% to 10% improvement in both relative pose error (RPE) and absolute trajectory error (ATE). These results demonstrate that the proposed VIO algorithm is an efficient solution suitable for low-computing platforms requiring real-time localization and navigation.