# Semi-Direct Point-Line Visual Inertial Odometry for MAVs
## 5 Conclusions  
In this paper, a novel, semi-direct, point-line, visual inertial odometry for MAVs was proposed, which extracts point-line features from the image and uses the semi-direct method to track keyframes and non-keyframes. The proposed method also adopts the sliding window strategy and uses the improved marginalization method to decompose the high-dimensional matrix step by step, according to the cost function, to optimize the solution. Experiments on the EuRoC datasets show that the accuracy and real-time performance of SDPL-VIO is better than that of other state-of-the-art VIO methods, especially in challenging datasets containing fast motion, large illumination changes or poorly textured environments. The SDPL-VIO performance, in terms of accuracy and efficiency, validated that it is suitable for navigational uses in MAVs with low-cost sensors. In future work, we aim to adopt a faster line detector that could be more conducive to continuous and real-time tracking.  
Conceptualization, B.G., B.L. and C.T.; Formal analysis, B.G. and C.T.; Investigation, B.G.; Methodology, B.G. and C.T.; Project administration, B.L.; Resources, B.L.; Software, B.G. and C.T.; Supervision, B.L.; Validation, B.G.; Writing--original draft, B.G.; Writing--review & editing, B.G. and C.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by National Natural Science Foundation of China under Grant 62171735, 62271397, 62173276, 62101458, 61803310 and 61801394, in part by the Natural Science Basic Research Program of Shanxi under Grant 2022 GY-097,2021 JQ-122 and 2021 JQ-693, and in part by China Postdoctoral Science Foundation under Grant 2020M673482 and 2020M673485.  
Not applicable.  
Figure 14: The comparison of translation errors on the MH03 dataset.  
Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MAVs & Micro Aerial Vehicles \\ IMU & Inertial Measurement Unit \\ VIO & Visual Inertial Odometry \\ VIN & Visual Inertial Navigation \\ OKVIS & Keyframe-based visual-inertial SLAM using nonlinear optimization \\ BRISK & BRISK: Binary robust invariant scalable keypoints \\ ORB & Oriented FAST and Rotated BRIEF \\ ORB-SLAM2 & ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and \\  & RGB-D Cameras \\ PL-SLAM & PL-SLAM: Real-time monocular visual SLAM with points and lines \\ DSO & Direct Sparse Odometry \\ VINS-Mono & VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator \\ PL-VIO & PL-VIO: Tightly-Coupled Monocular Visual-Inertial Odometry Using Point and \\  & Line Features \\ SVO & SVO: Fast semi-direct monocular visual odometry \\ PL-SVO & PL-SVO: Semi-direct Monocular Visual Odometry by combining points and \\  & line segments \\ SVL & Semi-direct monocular visual and visual-inertial SLAM with loop closure detection \\ PCSD-VIO & PC-SD-VIO: A constant intensity semi-direct monocular visual-inertial odometry \\  & with online photometric calibration \\ EuRoC & the European Robotics Challenge \\ SFM & Structure from Motion \\ FAST & the Accelerated Segment Test \\ KLT & Kanade-Lucas-Tomasi \\ RANSAC & Random Sample Consensus \\ LSD & Line Segment Detector \\ LBD & Line Band Descriptors \\ DBoW2 & Bags of Words \\ RMSE & root-mean-square error \\ \end{tabular}