The main contribution of this paper is to introduce a computationally efficient iterative closest line (ICL) algorithm for determining indoor position drift of a quadcopter using minimal lidar data. In addition, we present the system-level design and implementation of a new quadcopter both as hardware and flight control algorithms. Such a platform allows us to develop and experiment new control and system optimization techniques. As an example, we discuss how the proposed ICL algorithm is used for position hold and control purposes by plugging it into the low level implementation of the flight control algorithm of the quadcopter. For testing and validation we use simulations with real world data. As part of the system-level design aspects, we present an investigation of the quadcopter power consumption. We are interested in power consumption because it is the major factor that determines the flight time of a typical quadcopter. We believe that this work is a contribution toward achieving better quadcopter design and control for indoor autonomous navigation.
System Level Design of RF Receivers Based on Non Linear Optimization and Power Consumption Models
