EIP-VIO: Edge-Induced Points Based Monocular Visual-Inertial Odometry

Edge and line features boost the performance of simultaneous localization and mapping (SLAM) systems while bring more additional computational cost. In this paper, we propose an improved and practical monocular visual-inertial odometry method based on selective edge points. Focus on solving the issues of mismatching and tracking loss, the auxiliary edge is utilized to supervise the selection of features instead of using rough features directly. Features along edges are employed as stronger keypoints to steer point-selection away from less distinctive points. By combining matched edge-guided feature based method, selective sampling keypoints along edges based direct method and inertial measurement unit (IMU), the performance of absolute pose estimation is more accurate and efficient. In addition, a weight adjustment factor scheme before IMU error term is introduced in our optimization model to further reduce the influence of motion blur and less texture. Experimental results on public benchmark dataset demonstrate that the proposed method is more efficient and effective compared with the state-of-the-art methods.