Wide Field Integration (WFI) is a biologically inspired method of spatially decomposing optic flow estimates to extract relevant behavioral cues for navigation. In this thesis, a framework is developed that allows the direct application of a Kalman filter to improve the state information extracted from optic flow measurements. In addition, the noise properties of optic flow measurements are characterized, and an architecture to propagate the uncertainty in optic flow measurements to WFI state estimates is formalized. The closed-loop performance of a ground robot maneuvering in a straight tunnel using WFI outputs is then analyzed using three different algorithms to compute optic flow. The performance of the robot is characterized by its ability to track the tunnel centerline, and the accuracy of the WFI state estimates are compared with the true state estimates using a visual motion capture system. Lastly, the Kalman filter is implemented on a ground robot and the modified closed-loop performance is analyzed.