Vehicle egomotion estimation using computer vision

Abstract

A vision based navigation alter is developed for application on UAVs and tested in simulation. This alter is meant to allow the UAV to navigate in GPS-denied environments using measurements from a suite of cameras. The extended Kalman alter integrates measurements from multiple non-overlapping cameras as well as an IMU and occasional GPS. Simulations are conducted to evaluate the performance of the alter in a variety of fight regimes as well as to assess the value of using multiple cameras. Simulations demonstrate the value of using multiple cameras for egomotion estimation. Multiple non-overlapping cameras are useful for resolving motion in an unobservable direction that manifests as an ambiguity between translation and rotation. Additionally, multiple cameras are extremely useful when flying in an environment such as an urban canyon, where features remain in the fields of view for a very short period of time.