  Autonomous mobile robot competitions judge based on a robot's ability to
quickly and accurately navigate the game field. This means accurate
localization is crucial for creating an autonomous competition robot. Two
common localization methods are odometry and computer vision landmark
detection. Odometry provides frequent velocity measurements, while landmark
detection provides infrequent position measurements. The state can also be
predicted with a physics model. These three types of localization can be
"fused" to create a more accurate state estimate using an Extended Kalman
Filter (EKF). The EKF is a nonlinear full-state estimator that approximates the
state estimate with the lowest covariance error when given the sensor
measurements, the model prediction, and their variances. In this paper, we
demonstrate the effectiveness of the EKF by implementing it on a 4-wheel
mecanum-drive robot simulation. The position and velocity accuracy of fusing
together various combinations of these three data sources are compared. We also
discuss the assumptions and limitations of an EKF.
