Indoor navigation systems for unmanned aerial vehicles

Abstract

This thesis aims to develop an advanced indoor navigation system for unmanned aerial vehicles. Two different UAV platforms have been developed as test beds for the study, namely a coaxial helicopter with a compact footprint and a quadrotor helicopter with larger payload. Modeling and design of flight control laws have been done successfully for both platforms. With the help of the onboard camera and laser scanner sensors, both visual and laser-based odometry methods have been implemented to solve the GPS-denied condition in an indoor environment. To get a better drift-free position estimation and to reconstruct a map along the UAV path, a simultaneous localization and mapping technique is explored in breadth and depth. An innovative FastSLAM algorithm in cooperating both corner and line features have been proposed and tested with great success. It is found that when indoor environment is partially known, a much more robust and efficient localization method can be implemented onboard of the UAV with a few reasonable assumptions. The developed UAV indoor navigation system has been verified in numerous flight tests and helped the Unmanned Aircraft Systems Group from the National University of Singapore win the overall championship in the 2013 Singapore Amazing Flying Machine Competition.