ImageNAV-LZ: Autonomous Landing Zone Detection

Small Unmanned Autonomous Vehicles (SUAS) are well suited for ISR (Intelligence Surveillance and Reconnaissance) applications. The mission effectiveness of SUAS for ISR can be improved if the SUAS can perch at vantage points, continuing to perform ISR while conserving power. One of the primary bottlenecks to autonomous perching and re-launching is the ability to identify suitable landing zones. The complexity of the urban landscape, combined with the limited processing power on board the SUAS, make this a challenging problem. Scientific Systems Company Inc. (SSCI) in partnership with Brigham Young University (BYU) proposes ImageNav-LZ for autonomous landing zone detection for SUAS. ImageNav-LZ utilizes an innovative spiral search strategy starting just outside the maximum grazing angle of a target surveillance region. As it continues on the spiral, candidate landing-zones are identified when they cast a shadow on the surveillance area. The candidate landing zones are then evaluated for suitability for landing (area, terrain type, slope), suitability for ISR (line of sight visibility of target area) and suitability for re-launch (potential energy, path for take-off, vertical drop etc.). ImageNav-LZ spiral search strategies and efficient vision processing algorithms will significantly reduce the computational burden for landing zone determination.