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 moving observer makes noisy, sampled measurements on the spatially-distributed flow-field surrounding him, and, on the basis of these measurements, generates an estimate of his own linear and angular velocity which optimally satisfies, in a least-squares sense, the visual kinematic flow constraints. A subsidiary output of the model is an "impact time" map, an observer-centered spatially-sampled scaled replica of the viewed surface. Simulations are presented to demonstrate parametric sensitivity and ability to model relevant human visual performance data.