Integration of Image-Based and Artificial Intelligence Algorithms: A Novel Approach to Personal Navigation

Navigation systems, such as the Global Positioning System (GPS) and inertial measurement units (IMUs) become miniaturized and cost effective, enabling their fusion in a portable, low-cost navigation device for individual users, supporting predominantly outdoor navigation. This paper presents an unconventional solution designed for indoor–outdoor navigation, based on integration of GPS, IMU, digital barometer, magnetometer compass, and human locomotion model handled by Artificial Intelligence (AI) techniques that form an adaptive knowledge-based system (KBS). KBS is trained during the GPS signal availability, and is used to support navigation under GPS-denied conditions. A complementary technique used in our solution, which supports indoor navigation, is the image-based technique that uses a Flash LADAR sensor. Navigation from 3D Flash LADAR scene reconstruction utilizes the range distance to static features common in images acquired from two separate locations, which allows for triangulating the user’s position. By combining Flash LADAR image with the IMU data, a linear feature-based algorithm that identifies common static features between two images, along with the error estimates, is facilitated. Since the algorithm is based on linear methodologies, it enables rapid processing while generating robust, accurate position and error estimation data. In this paper, system design, as well as a summary of the performance analysis in the mixed indoor–outdoor environments is presented.