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The aim to colonize extra-terrestrial planets has been of great interest in recent years. For Mars, or other planets to sustain human life, it is essential that water is present and accessible. Mars may contain or provide signs of the possibility of near surface water, but this work will only focus on surface water for the purposes of simulation. In this paper, we present a method for autonomous navigation and detection of surface water bodies in the GPS denied environments of Mars via a fully autonomous UAV. A combination of existing state-of-the-art tools and techniques have been utilized to enable the development of this system. Additionally, we create a modular framework to simulate the mission using the AirSim simulation environment and the Robot Operating System (ROS). The simulation environment is leveraged by using the Unreal game engine running in Windows OS, interfacing with an open source software for simultaneous localization and mapping (SLAM), running on ROS and Linux OS. A simulated mission was successfully implemented and demonstrated using the framework. Results obtained indicate that the framework enables the navigation of a UAV in the simulated Mars environment and allows the UAV to detect surface water bodies. The developed simulation framework, along with the knowledge and techniques attained in this research, could accelerate the development, testing and deployment of missions for a real-world Mars UAV, for the detection of surface water bodies. Additionally, this research aims to support and build upon prior work to further aid the search for water on other planets, as well as assisting humans in becoming a multi-planet species.

This paper proposes an approach to obtain a localisation that is robust to smoke by exploiting multiple sensing modalities: visual and infrared (IR) cameras. This localisation is based on a state-of-the-art visual SLAM algorithm. First, we show that a reasonably accurate localisation can be obtained in the presence of smoke by using only an IR camera, a sensor that is hardly affected by smoke, contrary to a visual camera (operating in the visible spectrum). Second, we demonstrate that improved results can be obtained by combining the information from the two sensor modalities (visual and IR cameras). Third, we show that by detecting the impact of smoke on the visual images using a data quality metric, we can anticipate and mitigate the degradation in performance of the localisation by discarding the most affected data. The experimental validation presents multiple trajectories estimated by the various methods considered, all thoroughly compared to an accurate dGPS/INS reference.