Optimal vision aided navigation in GPS-denied regions

Abstract

The US Department of Defense has a need to successfully navigate in operational regions where GPS is degraded or denied. When GPS is denied, navigation of aerial platforms, including manned and unmanned aerial vehicles (UAVs), for Intelligence, Surveillance, and Reconnaissance (ISR) missions, targeting missions, or autonomous cargo delivery missions, becomes compromised. In the absence of GPS, navigating from pure inertial solutions leads to rapidly growing position errors due to drift in the inertial measurement unit. Vision Aided Navigation (VAN) approaches can aid the inertial solution to reduce navigation error, but require salient and distinct scene content for image alignment. In this paper, we present an approach to optimal path planning for VAN over operational ground regions that minimizes navigation position error. The approach uses automated pre-mission visual fiducial discovery to identify regions in imagery of the fly-over area that contain unique, salient, discriminative, and stable feature content. The discovered visual fiducial regions are used to form a map of probabilities of successful VAN at each point of the gridded fly-over region. An optimal path planning algorithm uses the probability map to determine the path over the fly-over region that maximizes navigability and minimizes VAN positioning error. Constraints, such as no-fly zones and path length constraints, are incorporated into the formulation to generate a constrained optimization problem. We present the mathematical formulation of the constrained path planning optimization problem and generate numerical results demonstrating performance.