A Visual-Inertial Odometry Using EKF
Visual-inertial odometry (VIO) is a process of estimating the position and orientation of an mobile platform (e.g., vehicle, robot) using measurements from on-board cameras and IMU sensor. In this paper, a visual-inertial odometry algorithm is presented which can achieves accurate performance. In the proposed method, relative position and orientation between the body frames at different time instants are estimated by MIMU and monoVO separately, then an extended Kalman filter (EKF) is used to combining these estimation for achieving an accurate estimation of the motion. Experiment is conducted to show the effectiveness of the proposed method in real environment. Introduction Visual and inertial measurements offer complementary properties which make them particularly suitable for fusion, in order to address robust and accurate localization and mapping, a primary need for any mobile robotic system [1]. In general, there are two main ways to approaching the visual-inertial estimation problem: nonlinear optimization methods and filtering methods [2]. Although the nonlinear optimization method can achieve higher accuracy, it is difficult to run in real time due to its high computational cost. Based on this reason, an EKF is used for sensor fusion in the proposed method. The EKF framework consists of a prediction step and an updating step. In the prediction step, MIMU provides acceleration and rotational velocity measurements in three axes, and makes the motion prediction. In the updating step, monocular camera provides the angular and ranging measurements between the camera frames at different time instants, and uses these measurements to update the prediction results. In the following, we describe the algorithm in detail. MIMU Dynamic Model An IMU state vector at any time instant can be defined by a 16×1 vector,           T T T T T T W W IMU WI WI WI a g , , , ,        x p q v b b (1) where W WI p and W WI v are the position and velocity of IMU frame {I} with respect to world frame {W}, expressed in {W}. qWI is the unit quaternion describing the rotation from {W} to {I}, ba and bg are 3×1 vectors that describe the biases affecting the accelerometer and gyroscope measurements, respectively. For MIMU, the real angular velocity ω and the real acceleration a are related with gyroscope and accelerometer measurements in the following form: m g m a     ω ω b a a b (2) The dynamic model can be represented by the form: