EDI: ESKF-based Disjoint Initialization for Visual-Inertial SLAM Systems
Weihan Wang Jiani Li Yuhang Ming Philippos Mordohai
Visual-inertial initialization can be classified into joint and disjoint
approaches. Joint approaches tackle both the visual and the inertial parameters
together by aligning observations from feature-bearing points based on IMU
integration then use a closed-form solution with visual and acceleration
observations to find initial velocity and gravity. In contrast, disjoint
approaches independently solve the Structure from Motion (SFM) problem and
determine inertial parameters from up-to-scale camera poses obtained from pure
monocular SLAM. However, previous disjoint methods have limitations, like
assuming negligible acceleration bias impact or accurate rotation estimation by
pure monocular SLAM. To address these issues, we propose EDI, a novel approach
for fast, accurate, and robust visual-inertial initialization. Our method
incorporates an Error-state Kalman Filter (ESKF) to estimate gyroscope bias and
correct rotation estimates from monocular SLAM, overcoming dependence on pure
monocular SLAM for rotation estimation. To estimate the scale factor without
prior information, we offer a closed-form solution for initial velocity, scale,
gravity, and acceleration bias estimation. To address gravity and acceleration
bias coupling, we introduce weights in the linear least-squares equations,
ensuring acceleration bias observability and handling outliers. Extensive
evaluation on the EuRoC dataset shows that our method achieves an average scale
error of 5.8% in less than 3 seconds, outperforming other state-of-the-art
disjoint visual-inertial initialization approaches, even in challenging
environments and with artificial noise corruption.