Future Mars Rotorcrafts require advanced navigation capabilities to enable all terrain access for science investigations with long distance flights that are executed fully autonomously. A critical component is the ability to safely land in hazardous terrain as part of a mission, or triggered by an emergency situation. In this paper, we present an advanced navigation system for continuous on-board terrain reconstruction for the purpose of hazard-free landing site detection for the autonomous navigation of a Mars Science Helicopter - a JPL research concept that investigates the feasibility of flying a multi-kilogram science payload at various Mars science locations, with flight ranges of multiple kilometers per flight. Our approach builds on a vision-based perception system that incorporates an on-board visual-inertial state estimator augmented by a laser altimeter (range-VIO), and a structure-from-motion 3D reconstruction approach that uses a single, downward-looking camera to provide dense depth measurements while the vehicle is in motion. Depth measurements are accumulated in a local, robot-centric, multi- resolution elevation map that is analyzed by a landing site detector to extract safe landing areas below the rotorcraft, based on a heuristic that includes slope, roughness and the presence of landing hazards. Detected landing sites are prioritized by an on-board autonomy engine that either selects suitable landing sites for immediate landing maneuvers, or can explore a terrain location as part of a mission in order to find a best landing site in a pre-planned area. We demonstrate and evaluate our approach on simulated data and data acquired with a surrogate unmanned aerial system (UAS) executing flights over relevant terrain.


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    Title :

    Autonomous Safe Landing Site Detection for a Future Mars Science Helicopter


    Contributors:


    Publication date :

    2021-03-06


    Size :

    16065464 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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