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SLC-VIO: a stereo visual-inertial odometry based on structural lines and points belonging to lines

Published online by Cambridge University Press:  17 January 2022

Chenchen Wei Open the ORCID record for Chenchen Wei [Opens in a new window] ,
Yanfeng Tang ,
Lingfang Yang  and
Zhi Huang
Chenchen Wei
Affiliation:
College of Mechanical and Vehicle Engineering, Hunan University, 2nd South Lushan Road, 410009, Changsha, China
Yanfeng Tang
Affiliation:
College of Mechanical and Vehicle Engineering, Hunan University, 2nd South Lushan Road, 410009, Changsha, China
Lingfang Yang
Affiliation:
College of Civil Engineering, Hunan University, 2nd South Lushan Road, 410009, Changsha, China
Zhi Huang*
Affiliation:
College of Mechanical and Vehicle Engineering, Hunan University, 2nd South Lushan Road, 410009, Changsha, China
*
*Corresponding author. E-mail: huangzhi@hnu.edu.cn
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Abstract

To improve mobile robot positioning accuracy in building environments and construct structural three-dimensional (3D) maps, this paper proposes a stereo visual-inertial odometry (VIO) system based on structural lines and points belonging to lines. The 2-degree-of-freedom (DoF) spatial structural lines based on the Manhattan world assumption are used to establish visual measurement constraints. The property of point belonging to a line (PPBL) is used to initialize the structural lines and establish spatial distance-residual constraints between point and line landmarks in the reconstructed 3D map. Compared with the 4-DoF spatial straight line, the 2-DoF structural line reduces the variables to be estimated and introduces the orientation information of scenes to the VIO system. The utilization of PPBL makes the proposed system fully exploit the prior geometric information of environments and then achieves better performance. Tests on public data sets and real-world experiments show that the proposed system can achieve higher positioning accuracy and construct 3D maps that better reflect the structure of scenes than existing VIO approaches.

Keywords

point belonging to a lineManhattan world assumptionstructural line featuressimultaneous localization and mapping (SLAM)visual-inertial odometry (VIO)

Information

Type
Research Article
Information
Robotica , Volume 40 , Issue 8 , August 2022 , pp. 2765 - 2785
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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