Monocular Visual-Inertial State Initialization for Micro Aerial Vehicles

Abstract

Monocular visual-inertial state estimator can be classified into filterbased and optimization-based method. However, both of these methods require an initial state to bootstrap the system. In this paper, we propose a robust initialization algorithm to provide high-quality initial guess for the monocular visual-inertial system (VINS). The proposed method takes the up-to-scale camera poses estimated by monocular vision-only SLAM (simultaneous localization and mapping) and the IMU sensor measuements as input. The gyroscope bias are firstly estimated by minimizing the error between pre-integrated gyroscope measurements and camera attitude measurements. After that, a rough gravity vector is calculated by aligning the preintegrated measurements and camera translation measurements. The observable of the accelerometer bias is also checked in this step. The gravity vector is refined in the third step, with the velocity is estimated in this step as well as the acceleration bias if it is observable. The practicability of the proposed approach is validated by simulation and real datasets experiments.