Sensor Data Fusion Using Unscented Kalman Filter for VOR-Based Vision Tracking System for Mobile Robots

Abstract

This paper presents sensor data fusion using Unscented Kalman Filter (UKF) to implement high performance vestibulo-ocular reflex (VOR) based vision tracking system for mobile robots. Information from various sensors is required to be integrated using an efficient sensor fusion algorithm to achieve a continuous and robust vision tracking system. We use data from low cost accelerometer, gyroscope, and encoders to calculate robot motion information. The Unscented Kalman Filter is used as an efficient sensor fusion algorithm. The UKF is an advanced filtering technique which outperforms widely used Extended Kalman Filter (EKF) in many applications. The system is able to compensate for the slip errors by switching between two different UKF models built for slip and no-slip cases. Since the accelerometer error accumulates with time because of the double integration, the system uses accelerometer data only for the slip case UKF model. Using sensor fusion by UKF, the position and orientation of the robot is estimated and is used to rotate the camera mounted on top of the robot towards a fixed target. This concept is derived from the vestibuloocular reflex (VOR) of the human eye. The experimental results show that the system is able to track the fixed target in various robot motion scenarios including the scenario when an intentional slip is generated during robot navigation.

Multi-baseline phase unwrapping algorithm for INSAR

“…In (28), d i (a,s) denotes the optimal weight of the ith particle of pixel (a, s), and can be expressed as…”

“…The posterior mean value and the variance of the state variable can be obtained by using unscented transformation (UT) to set up the minimal set of deterministic sigma points of the state variable [26][27][28][29]. Suppose that P x and N are the estimation error covariance of the state variable x and the dimension of the state variable x, respectively, then the sigma points of the state variable x can be written as [5] …”

The navigation method of wheeled mobile robot based on data fusion obtained from onboard sensors and camera

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