During the initial phase of a disaster response it is essential for responders to quickly and safely assess the overall situation. The use of rescue robots that can autonomously navigate and map these environments can help responders realize this goal while minimizing danger to them. In order for rescue robots to be of service to the responders, they must be able to sense the environment, create an internal representation that identifies victims and hazards to responders, and provide an estimate of where they are and where they have been. Methods for developing a stable navigation solution are based on sensors that can be broadly classified into two approaches, absolute (exteroception) and relative (proprioception). Commonly, two or more of these approaches are combined to develop a stable navigation solution that is insensitive to and robust in the presence of the errors that plague partial solutions by taking into account errors in the vehicle's pose, thus bounding the uncertainty in the navigation solution. Since the capabilities and limitations of these approaches vary, it is essential for developers of robotic systems to understand the performance characteristics of methodologies employed to produce a stable navigation solution. This paper will provide quantitative analysis of two proprioceptive approaches, namely encoder-based odometry and inertial navigation system, and an exteroceptive approach namely visual odometry that uses scan matching techniques.