Simulated Vision-based Approach and Landing System for Advanced Air Mobility

Advanced Air Mobility (AAM) aircraft require precision approach and landing systems
(PALS) in several environments, such as urban, suburban, and rural. It is challenging to
implement current state-of-the-art methods approved for automated approach and landing
for AAM operations with challenges such as GPS degradation in urban environments and
visual navigation aids like the glideslope and localizer being narrow and not allowing alternative
incoming landing angles at vertiports. However, existing technology and systems, i.e., the
instrument landing system (ILS) with glideslope and localizer indicators that use vision, IR,
radar, or GPS methods, provide baseline perception and sensing requirements for AAM
aircraft approach and landing. This paper focuses on vision-based PAL and computer vision
feature correspondence methods to demonstrate a baseline navigation system while adhering
to the Federal Aviation Administration requirements and regulations about heliport design
(FAA AC 150/5390-2C), which is one of the closest references for vertiport requirements and
regulations. The coplanar pose from orthography and scaling with iterations (COPOSIT)
algorithm determines pose estimation, which feeds into an Extended Kalman filter that combines
IMU with vision to create a vision-based approach and landing (VAL) sensor fusion navigation
solution for GPS-denied environments. The VAL navigation solution provides promising
simulation results for AAM PALS with Hough circle detection and feature correspondence,
which demonstrate robustness to false positives. This paper incorporates moderately high-
fidelity simulations with computer graphics rendering to show a distributed sensor network to
track an AAM aircraft during approach and landing to compare with the aircraft’s onboard
vision-based navigation solution.