Non Linear Tracking Using Unscented Kalman Filter

Accurate localization of mobile robots to locate its position and orientation is of key importance since it enables a mobile robot to navigate properly in any given environment. Various techniques of localization used are such as GPS/GNSS, IMU sensors or by using odometric measurements. However each of these techniques suffers from various drawbacks. Dead-reckoning (DR) is a popular client to get precise localization information. DR estimates the current position based on the previous positions observed over a span of time. However DR depends on encoder and odometric information which are subject to major errors due to surface roughness, wheel slippage and tolerance rate of the machine which leads to an accumulation of errors. Many researchers have addressed this problem by adding certain external sources such as encoded magnetic compass, rate-gyros etc., However addition of these sensors has led to various new errors. In this paper, the use of unscented Kalman filter (UKF) is proposed along with the DR to get accurate localization information. UKF uses a deterministic sampling approach that captures the estimates of mean and covariance with a set of sigma points. The simulation results show that the proposed method is able to track the desired path with least error when compared to DR used alone. The localization of a mobile robot with the proposed system is also highly reliable.