A model for visual flow-field cueing and self-motion estimation

A computational model for visual flow-field cueing and self-motion estimation is developed and simulated. The model is predicated on the notion that the pilot makes noisy, sampled measurements on the spatially distributed visual flow-field surrounding him, and, on the basis of these measurements, generates estimates of his own linear and angular terrain-relative velocities which optimally satisfy, in a least-squares sense, the visual kinematic flow constraints. A subsidiary but significant output of the model is an “impact time” map, an observer-centered spatially scaled replica of the viewed surface. Simulations are presented to demonstrate the parametric sensitivity and ability to model relevant human visual performance data.