An autonomous vehicle should recognize its position when the vehicle is moving, otherwise the vehicle would not operate correctly. In addition, the “kidnapping“ problem sometimes appears when the vehicle starts at an unknown position. The theoretical basis of the solution to this problem, known as simultaneous localization and mapping (SLAM), is now well understood. A number of approaches to SLAM have appeared in the recent literature. The SLAM algorithm consists of two methods. One is a map-building method that makes a map to represent the environment. The other is a mapping method to compute the position of the vehicle in absolute coordinates. However, it is difficult to apply the SLAM algorithm to an actual autonomous vehicle because it needs a certain amount of operating time. In this article, we explain the use of ultrasonic sensors for detecting obstacles in corridors, and a digital magnetic compass, a gyro, and two encoders for immediate localization. Experiments showed that this algorithm can be executed with a high degree of accuracy and reliability in an unknown environment. This algorithm could also solve the “kidnapping“ problem in a fast operating time.