UAV navigation in GPS-denied environment using particle filtered RVL

Abstract

Unmanned aerial vehicles have become widespread in today’s world and are used for applications ranging from real estate marketing and bridge inspection to defense and military applications. These applications have in common some form of autonomous navigation that requires a good localization capability at all time. Most UAV are using a combination of global navigation satellite systems (GNSS) and inertial measurement unit (IMU) to perform this task. Unfortunately, GNSS are subject to signal unavailability and all sorts of interference impeding on the ability of the UAV to self-localize. In this paper, we propose a new algorithm to perform localization in GNSS-denied environments by using a relative visual localization technique. We developed a new measure based on the use of local feature points extracted with ORB to estimate the likelihood of a previously captured image to have been taken in a position close to the current UAV location. The measure is embedded in a particle filter in which IMU data is used in order to reduce the number of images we need to analyze to perform localization. The resulting method have shown significant improvement in both accuracy and execution time in comparison to previous approaches.