TY - JOUR
T1 - Lightweight omnidirectional visual-inertial odometry for MAVs based on improved keyframe tracking and marginalization
AU - Gao, Bo
AU - Lian, Baowang
AU - Tang, Chengkai
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
PY - 2024/11
Y1 - 2024/11
N2 - Due to the limited onboard resources on Micro Aerial Vehicles (MAVs), the poor real-time performance has always been an urgent problem to be solved in the practical applications of visual inertial odometry (VIO). Therefore, a lightweight omnidirectional visual-inertial odometry (LOVIO) for MAVs based on improved keyframe tracking and marginalization was proposed. In the front-end processing of LOVIO, wide field-of-view (FOV) images are captured by an omnidirectional camera, frames are tracked by semi-direct method combining of direct method with rapidity and feature-based method with accuracy. In the back-end optimization, the Hessian matrix corresponding to the error optimization equation is stepwise marginalized, so the high-dimensional matrix is decomposed and the operating efficiency is improved. Experimental results on the dataset TUM-VI show that LOVIO can significantly reduce running time consumption without loss of precision and robustness, that means LOVIO has better real-time and practicability for MAVs.
AB - Due to the limited onboard resources on Micro Aerial Vehicles (MAVs), the poor real-time performance has always been an urgent problem to be solved in the practical applications of visual inertial odometry (VIO). Therefore, a lightweight omnidirectional visual-inertial odometry (LOVIO) for MAVs based on improved keyframe tracking and marginalization was proposed. In the front-end processing of LOVIO, wide field-of-view (FOV) images are captured by an omnidirectional camera, frames are tracked by semi-direct method combining of direct method with rapidity and feature-based method with accuracy. In the back-end optimization, the Hessian matrix corresponding to the error optimization equation is stepwise marginalized, so the high-dimensional matrix is decomposed and the operating efficiency is improved. Experimental results on the dataset TUM-VI show that LOVIO can significantly reduce running time consumption without loss of precision and robustness, that means LOVIO has better real-time and practicability for MAVs.
KW - Keyframe tracking
KW - Lightweight
KW - Marginalization
KW - Omnidirectional camera
KW - Visual-inertial odometry
UR - http://www.scopus.com/inward/record.url?scp=85201297780&partnerID=8YFLogxK
U2 - 10.1007/s11235-024-01208-4
DO - 10.1007/s11235-024-01208-4
M3 - 文章
AN - SCOPUS:85201297780
SN - 1018-4864
VL - 87
SP - 723
EP - 730
JO - Telecommunication Systems
JF - Telecommunication Systems
IS - 3
ER -