Application Challenges from a Bird's‐Eye View

Abstract

This chapter describes the challenges of global pose constraints (GPS)-denied autonomous navigation of drones. It presents techniques based on visual-odometry and simultaneous localization and mapping (SLAM) technologies as a viable replacement of laser-based navigation. Micro aerial vehicles (MAVs) can be seen as the logical extension of ground mobile robots. Their ability to fly allows them to easily avoid obstacles on the ground and to have an excellent bird's-eye view. Most works on vision-based reactive navigation of MAVs have relied on biologically inspired vision algorithms, such as optical flow, Hrabar and Sukhatme, Ruffier and Franceschini, and Zufferey. Experimental results demonstrating three MAVs navigating autonomously in an unknown GPS-denied environment and performing 3D mapping and optimal surveillance coverage were presented. The chapter reviews works dealing with safety and robustness of MAVs using mainly vision sensors, the state-of-the-art research on failure recovery and emergency landing.