Low Altitude Airborne SLAM with INS Aided Vision System

Abstract

Main goal of navigation-system design is to select a suite of sensors and a computational approach that allows the accuracy, update rate, and bandwidth specifications to be achieved in a cost-effective manner. Today, most of the navigation systems rely on inertial sensors measurements or GPS measurements. There are many difficulties which are present in the INS. Visual information is the must nowadays in terms of navigation and guidance measurements for autonomous aerial and ground vehicles. This visual information can be efficiently used in the Simultaneous Localization and Map building (SLAM) problem. In this paper, we have developed a complete effective airborne SLAM algorithm with INS and Visual systems by implementing an Extended Kalman Filter (EKF). Here we have proposed a solution to the well-known shortcomings that complicate the application of the EKF in large, real-world environments by removing the landmarks from the EKF and creating a special module called map management. We proposed a methodology based on circle intersections and we demonstrate the effectiveness of this methodology along with all the modules of our SLAM algorithm with virtual images taken from a downward looking virtual camera. I. Introduction fficient localization and mapping algorithms are vital for autonomous operations of unmanned systems. For many civilian and military aerial applications, the capabilities of autonomous operation without any or with minimal human intervention are of major importance. Simultaneous localization and map building (SLAM) problem continues to draw considerable attention of the research community due to the advantages it can offer, if efficiently solved, in building autonomous systems. SLAM examines the ability of an autonomous vehicle, starting in an unknown environment, to incrementally build an environment map and simultaneously localize itself within this map. A large number of research work related to SLAM were published in 2, 3, 5, 6, 7, 8 and proposing probabilistic- based solutions able to process noisy measurements (sensing errors) to be turned into accurate map. In this paper, we describe a technique dealing with airborne SLAM, where onboard sensors like INS and vision are used. A use of Extended Kalman Filter (EKF) is proposed without an increase of the number of estimated states that usually pose a problem and complicate the application of the EKF for SLAM in large, real-world environments. Landmarks are not directly included in the EKF estimation process as we created a special module called map management dealing with map measurements. In this module we proposed and implemented methodology what we called it, circle intersection. We demonstrated the effectiveness of the implemented methodology and our SLAM algorithm with virtual images taken from a downward virtual camera system. This paper is organized as follows. In section 2, we detail about the SLAM problem by presenting up to date related work in this active area. Section 3 is reserved for the definition of the Inertial/Vision EKF estimation algorithm. In Section 4 simulation experiments and results are given followed by a short conclusion and future work in section 5.