This paper presents our solution for enabling a quadrotor helicopter, equipped with a laser rangefinder sensor, to autonomously explore and map unstructured and unknown indoor environments. While these capabilities are already commodities on ground vehicles, air vehicles seeking the same performance face unique challenges. In this paper, we describe the difficulties in achieving fully autonomous helicopter flight, highlighting the differences between ground and helicopter robots that make it difficult to use algorithms that have been developed for ground robots. We then provide an overview of our solution to the key problems, including a multilevel sensing and control hierarchy, a high-speed laser scan-matching algorithm, an EKF for data fusion, a high-level SLAM implementation, and an exploration planner. 1 Finally, we show experimental results demonstrating the helicopter's ability to navigate accurately and autonomously in unknown environments. Micro Aerial Vehicles (MAVs) are increasingly being used in military and civilian domains, including surveillance operations, weather observation, and disaster relief coordination. Enabled by GPS and MEMS inertial sensors, MAVs that can fly in outdoor environments without human intervention have been developed [2, 3, 4, 5] . Unfortunately, most indoor environments and many parts of the urban canyon remain without access to external positioning systems such as GPS. Autonomous MAVs today are thus limited in their ability to fly through these areas. Traditionally, unmanned vehicles operating in GPS-denied environments can rely on dead reckoning for localization, but these measurements drift over time. Alternatively, simultaneous localization and mapping (SLAM) algorithms build a map of the environment around the vehicle while simultaneously using it to estimate the vehicle's position. Although there have been significant advances in developing accurate, drift-free SLAM algorithms for large-scale environments, these algorithms have focused almost exclusively on ground or underwater vehicles. In contrast, attempts to achieve the same results with MAVs have not been as successful due to a combination of limited payloads for sensing and computation, coupled with the fast, unstable dynamics of the air vehicles. In this work, we present our quadrotor helicopter system, shown in Figure 1 , that is capable of autonomous flight in unstructured indoor environments, such as the one shown in Figure 2 . The system employs a multi-level sensor processing hierarchy designed to meet the requirements for controlling a helicopter. The key contribution of this paper is the development of a fully autonomous quadrotor that relies only on onboard sensors for stable control without requiring prior maps of the environment. After discussing related work in Section 2, we begin in Section 3 by analyzing the key challenges MAVs face when attempting to perform SLAM. We then give an overview of the algorithms employed by our system. Finally, we demonstrate our helicopter navigating autonomously in 3 different unstructured indoor environments.
Our paper is orthogonal to a recent work of Bachrach et al. REF , where the authors present a system for performing autonomous exploration and map acquisition in indoor environments.
Autonomous Flight in Unknown Indoor Environments
International Journal of Micro Air Vehicles
Engineering