Abstract
This paper aims at a semi-dense visual odometry system that is accurate, robust, and able to run realtime on mobile devices, such as smartphones, AR glasses and small drones. The key contributions of our system include: 1) the modified pyramidal Lucas-Kanade algorithm which incorporates spatial and depth constraints for fast and accurate camera pose estimation; 2) adaptive image resizing based on inertial sensors for greatly accelerating tracking speed with little accuracy degradation; and 3) an ultrafast binary feature description based directly on intensities of a resized and smoothed image patch around each pixel that is sufficiently effective for relocalization. A quantitative evaluation on public datasets demonstrates that our system achieves better tracking accuracy and up to about 2X faster tracking speed comparing to the state-of-the-art monocular SLAM system: LSD-SLAM. For the relocalization task, our system is 2.0X ~ 4.6X faster than DBoW2 and achieves a similar accuracy.
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This work was funded by the National Natural Science Foundation of China (Grant No. 61502188).
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Xin Yang received her PhD degree in University of California, Santa Barbara, U.S. in 2013. She worked as a Post-doc in Learning-based Multimedia Lab at UCSB (2013-2014). She is current associate professor of Huazhong University of Science and Technology School of Electronic Information and Communications. Her research interests include monocular simultaneous localization and mapping, augmented reality, and medical image analysis. She has published over 30 technical papers, including TPAMI, TVCG, MIA, ACM MM, MICCAI, ISMAR, etc., co-authored two books and holds 2 U.S. patents. Professor Yang is a member of IEEE and a member of ACM.
Tangli Xue received his BE degree from Huazhong University of Science and Technology (HUST), China in 2015. He is currently a graduate student of HUST, School of Electronic Information and Communications. His research interests include monocular simultaneous localization and mapping and augmented reality. He has published three technical papers, including ACM MM, ISMAR and CVM.
Hongcheng Luo received the BE degree from Huazhong University of Science and Technology (HUST), China in 2016. He is currently a graduate student of HUST, School of Electronic Information and Communications. His research interests include monocular simultaneous localization and mapping and augmented reality.
Jiabin Guo received the MS degree from Huazhong University of Science and Technology (HUST), China in 2017. His research interests include monoclular Visual-Inertial SLAM and augmented reality.
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Yang, X., Xue, T., Luo, H. et al. Fast and accurate visual odometry from a monocular camera. Front. Comput. Sci. 13, 1326–1336 (2019). https://doi.org/10.1007/s11704-018-6600-8
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DOI: https://doi.org/10.1007/s11704-018-6600-8