Globally Asymptotically Stable Sensor-Based Simultaneous Localization and Mapping

This paper presents the design, analysis, and experimental validation of a globally asymptotically stable (GAS) filter for simultaneous localization and mapping (SLAM), with application to unmanned aerial vehicles. The SLAM problem is formulated in a sensor-based framework and modified in such a way that the underlying system structure can be regarded as linear time varying for observability analysis and filter design purposes, from which a linear Kalman filter with GAS error dynamics follows naturally. The performance and consistency validation of the proposed sensor-based SLAM filter are successfully assessed with real data, acquired indoors, using an instrumented quadrotor.

I. Introduction

Reliable navigation and positioning of unmanned aerial vehicles (UAVs) are fundamental for any autonomous mission, particularly in unknown environments where absolute positioning systems are absent or unreliable. The motivation for this study arises from the usage of autonomous rotorcraft for automatic inspection of critical infrastructures and buildings, such as bridges, electric power lines, dams, construction areas, etc. Near these structures, the global positioning system signal may be unreliable or nonexistent, whereas the electromagnetic interference and the existence of ferromagnetic materials may degrade any magnetometer measurement to the point of becoming unusable. This is of special importance as these dynamically unstable vehicles have to work as close as possible to the inspection target. The use of aided navigation techniques, as proposed in this study using a simultaneous localization and mapping (SLAM) algorithm, aims to solve this problem in such a way that these sensors are made redundant.