An important element of rotorcraft UAV operations is safe landing area determination (SLAD), which is the ability to select desirable landing or load placement areas at unprepared sites. Effectively and reliably accomplishing this task would greatly enhance high-level autonomous capabilities in many operations such as search and rescue and resupply. This paper presents the results of quantitatively evaluating two SLAD algorithms using a new test method that incorporates a detailed survey of the test sites. These survey sites act as benchmarks against which the SLAD methods are compared. One SLAD algorithm is a new approach that uses laser range data to detect a set of potential landing points and uses fuzzy logic to rank them based on surface roughness, size, and terrain slope metrics. The second algorithm uses laser range data to optimize a performance index, based on sliding window statistics of surface slope and roughness over the landing zone, to select potential landing points. Flight-test data were collected at six sites ranging from simple to complex with multiple runs at each site. Both methods are evaluated based on their true-positive and false-positive rates and the consistency of their landing site selection.

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