This paper presents an algorithm for solving three challenges of autonomous navigation: sensor signal processing, sensor integration, and path-finding. The algorithm organizes these challenges into three steps. The first step involves converting the raw data from each sensor to a form suitable for real-time processing. Emphasis in the first step is on image processing. In the second step, the processed data from all sensors is integrated into a single map. Using this map as input, during the third step the algorithm calculates a goal and finds a suitable path from robot to the goal. The method presented in this paper completes these steps in this order and the steps repeat indefinitely. The robotic platform designed for testing the algorithm is a six-wheel mid-wheel drive system using differential steering. The robot, called Anassa II, has an electric wheelchair base and a custom-built top and it is designed to participate in the Intelligent Ground Vehicle Competition (IGVC). The sensors consist of a laser scanner, a video camera, a Differential Global Positioning System (DGPS) receiver, a digital compass, and two wheel encoders. Since many intelligent vehicles have similar sensors, the approach presented here is general enough for many types of autonomous mobile robots.