Designing a spatially aware, autonomous quadcopter using the android control sensor system

Gathering information for intelligence, surveillance, and reconnaissance (ISR) poses a risk to the human operators, namely the United States military and intelligence sectors. An autonomous drone that can perform advance ISR of enclosed spaces will significantly impact a variety of safety critical applications, including search and rescue. Current systems are limited to outdoor environments with access to global positioning systems (GPS) and are typically expensive, with custom engineering and proprietary interfaces. Our aim is to create indoor capability and utilize commercial off-the-shelf (COTS) subsystems to reduce cost and improve flexibility for diverse applications. The goal of this project is to develop a proof of concept design for a quadcopter that will create a map of an unknown, indoor space. We must develop a simultaneous localization and mapping (SLAM) algorithm for the quadcopter to create the map autonomously. The problem of both building a map of an unknown space and localizing within that space is termed SLAM. SLAM is frequently referred to as a chicken-and-egg problem, since accurate mapping requires knowledge of location, and vice versa. A SLAM algorithm must probabilistically relate environmental sensors and utilize a probabilistic motion model to converge to a most likely map of the environment and position of the robot. This project has four major parts: hardware, which includes integration of the sensors, quadcopter, and Android phone; command and control; the SLAM algorithm, which will run without a GPS; and a mobile application for viewing usable maps. We found both localization and mapping algorithms are adept at operating separately within a GPS obscured environment. Future steps include combining the localization and mapping algorithms into an optimized SLAM algorithm that will run efficiently on the Android phone.