Lie Group Modelling for an EKF-Based Monocular SLAM Algorithm

Remote Sensing

Damien Vivet¹  Gaël Pages¹  Samy Labsir² 

[1] Institut Supérieur de l’Aéronautique et de l’Espace (ISAE-SUPAERO), University of Toulouse, 31400 Toulouse, France;Thales LAS France, Rue Pierre Gilles de Gennes, 91470 Limours, France;

关键词: visual SLAM;    coded patterns;    camera model;    Kalman filtering;    optimization;    Lie groups;   

DOI  :  10.3390/rs14030571

来源: DOAJ

【 摘 要 】

This paper addresses the problem of monocular Simultaneous Localization And Mapping on Lie groups using fiducial patterns. For that purpose, we propose a reformulation of the classical camera model as a model on matrix Lie groups. Thus, we define an original-state vector containing the camera pose and the set of transformations from the world frame to each pattern, which constitutes the map’s state. Each element of the map’s state, as well as the camera pose, are intrinsically constrained to evolve on the matrix Lie group $SE\left(3\right)$. Filtering is then performed by an extended Kalman filter dedicated to matrix Lie groups to solve the visual SLAM process (LG-EKF-VSLAM). This algorithm has been evaluated in different scenarios based on simulated data as well as real data. The results show that the LG-EKF-VSLAM can improve the absolute position and orientation accuracy, compared to a classical EKF visual SLAM (EKF-VSLAM).

【 授权许可】

Unknown