Autonomous mobile robots that can effectively navigate unknown environments could be utilized for a wide range of applications, including search and rescue, disaster assessment, reconnaissance, or other tasks that would be risky or impossible for a human to perform. However, there are several technical challenges that hinder reliable operation of Unmanned Aerial Vehicles (UAVs) in these environments. Situational awareness must be maintained to prevent collision with obstacles and to ensure stable flight. This is particularly important for autonomous vehicles, where an operator is not present to provide input to the vehicle. In such cases, a UAV often requires some method of mapping the unknown environment and determining its location within the environment in order to accomplish its mission. Most UAVs utilize Global Positioning System (GPS) signals for localization. In addition to aiding navigation, the GPS position information is used to bound drift in position estimates caused by the integration of acceleration and angular rate sensors. Unfortunately, the availability of GPS signals in unknown environments is not assured, especially for Micro Air Vehicles (MAVs) being operated indoors. As a result, other sensors must be used to provide position information so that a vehicle can make corrections to its state estimate. This paper examines the use of a scanning laser range sensor and a sonar to augment an Inertial Measurement Unit (IMU) for vehicle state estimation. A simulation was developed which utilizes these sensors to perform Simultaneous Localization and Mapping (SLAM) in an indoor environment. Experimental results using a scanning laser and IMU to perform mapping are provided. Two different SLAM algorithms are tested and a comparison between the performance of each is provided.