Natural gas production, Qatar's driving economic force, plays an increasingly important role in addressing the world's energy needs. Detecting and responding to gas leaks in a gas production facility are critical activities due to safety and environmental concerns and the economic impact of lost feedstock and product. Current practice for detecting gas leaks is manual and results in monotonous, time intensive work. However, this makes it an ideal task for mobile robots. Automating gas leak detection will require several advances to the state of the art in mobile robotics. Principal among these is improved robustness in localization and mapping in a semi-structured environment, such as a gas production plant. Commercial solutions such as fiducials, embedded magnets, etc. require costly changes in infrastructure that are undesirable. Similarly, GPS/INS will not be viable due to overhanging infrastructure. Recent advances in mapping and localization (SLAM) show promise in providing a viable solution to this problem. We propose to harness and enhance the state of the art in SLAM technology to enable robust operations of a mobile robot in the environment of a gas plant. Specifically, we will explore methods that utilize colorized-range data as well as stereo and monocular imagery using low-cost odometry and IMU input to reduce the cost of the solution. In summary, we will develop a robust robotics solution to be integrated with a gas leak detection system inspection robot.