3 May 2018 Benchmarking a LIDAR obstacle perception system for aircraft autonomy
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Abstract
The limits of an unmanned aerial vehicle's (UAV) obstacle detection system place fundamental limits on the UAV's ability to fly safely. Any certification of aviation grade autonomy will require benchmarking of the obstacle perception sub-system and its effect on UAV performance. Consequently, as Near Earth Autonomy has built a state of the art lidar based obstacle perception system, it has also been developing benchmarks and performing ight tests to understand how the theoretical capabilities of its perception suite translates into operational limits on air frames using its perception suite.

This paper analyses these obstacle perception guarantees through the lens of flight testing Near Earth Autonomy's m4 perception suite. The m4 perception suite uses a scanning, nodding lidar to enable safe autonomous take off, flight, and landing. It was tested on a UH-1 helicopter as a part of the Office of Naval Research's (ONR) Autonomous Aerial Cargo/Utility System (AACUS) program. The m4 perception suite enables safe high altitude cruise flight by perceiving all large obstacles within an 800 meter range of the helicopter. As the helicopter nears the ground the perceptual guarantees required for cruise flight speeds are violated by the smallest, and most difficult obstacles: wires. The perception suite still enables safe flight by using specialized algorithms to detect wires up to 400 meters away while travelling at 30m/s. Through over 80 flights in 8 different locations, we test the obstacle perception assumptions, see how the assumptions change, and understand how m4's capabilities impact full autonomous helicopter performance.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Adam Stambler, Adam Stambler, Hugh Cover, Hugh Cover, Kyle Strabala, Kyle Strabala, } "Benchmarking a LIDAR obstacle perception system for aircraft autonomy", Proc. SPIE 10640, Unmanned Systems Technology XX, 106400N (3 May 2018); doi: 10.1117/12.2305215; https://doi.org/10.1117/12.2305215
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