Research on Initialization Algorithm for Visual-Inertial SLAM System

Jisuanji kexue yu tansuo

LIU Gang, GE Hongwei¹ 

[1] 1. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi, Jiangsu 214122, China 2. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China;

关键词: simultaneous localization and mapping (slam);    visual inertial alignment;    preintegration;    inertial navigation;    fisher information;   

DOI  :  10.3778/j.issn.1673-9418.2005043

来源: DOAJ

【 摘 要 】

Monocular vision and inertial simultaneous localization and mapping (SLAM) system is becoming more and more popular in practical engineering applications because it can achieve the complementarity in use scenarios and lower hardware cost. Recent results have shown that optimization-based fusion strategies outperform filtering strategies. However, the optimization-based SLAM algorithm of vision inertial navigation fusion is highly nonlinear, and its performance highly depends on the accuracy of the estimation of the initial parameters of the system state. The inertial measurement unit needs acceleration excitation, which means that it cannot start from the static state, but must start from the unknown motion state. Therefore, accurate estimation of the initial state is the key to the high robustness of the algorithm and the first step of the vision inertial fusion algorithm. By analyzing the pre integration algorithm of inertial measurement unit, an initialization estimation system based on convex optimization is derived, and the initial states are solved jointly considering the constraints of the gravity acceleration. More importantly, a novel method is proposed to determine the termination condition of the initialization algorithm by measuring the estimation effect with Fisher information, which improves the accuracy of the algorithm and shortens the initialization time. Experiments on Euroc dataset show that the new algorithm has a more precise and robust initial state.

【 授权许可】

Unknown