In this paper we describe the Open Vision Computer (OVC) which was designed to support high speed, vision guided autonomous drone flight. In particular our aim was to develop a system that would be suitable for relatively small-scale flying platforms where size, weight, power consumption and computational performance were all important considerations. This manuscript describes the primary features of our OVC system and explains how they are used to support fully autonomous indoor and outdoor exploration and navigation operations on our Falcon 250 quadrotor platform.

High speed navigation through unknown environments is a challenging problem in robotics. It requires fast computation and tight integration of all the subsystems on the robot such that the latency in the perception-action loop is as small as possible. Aerial robots add a limitation of payload capacity, which restricts the amount of computation that can be carried onboard. This requires efficient algorithms for each component in the navigation system. In this paper, we describe our quadrotor system which is able to smoothly navigate through mixed indoor and outdoor environments and is able to fly at speeds of more than 18 m/s. We provide an overview of our system and details about the specific component technologies that enable the high speed navigation capability of our platform. We demonstrate the robustness of our system through high speed autonomous flights and navigation through a variety of obstacle rich environments.

This work addresses the design and implementation of a filter that estimates the orientation of the body-fixed z axis and the velocity of a quadrotor UAV from the inertial measurement unit (IMU) given a known yaw. The velocity and attitude estimation is possible since the filter employs a linear drag model measuring the drag forces on the quadrotor through the IMU. These forces are functions of the robot's velocity and attitude. In addition, the filter estimates the linear drag parameters and thrust coefficient for the propellers. These parameters may be fed back into a controller to improve tracking performance. Experimental results are used to validate the proposed approach.

We address the problem of high speed autonomous navigation of quadrotor micro aerial vehicles with limited onboard sensing and computation. In particular, we propose a dual range planning horizon method to safely and quickly navigate quadrotors to specified goal locations in previously unknown and unstructured environments. In each planning epoch, a short-range planner uses a local map to generate a new trajectory. At the same time, a safe stopping policy is found. This allows the robot to come to an emergency halt when necessary. Our algorithm guarantees collision avoidance and demonstrates important advances in real-time planning. First, our novel short range planning method allows us to generate and re-plan trajectories that are dynamically feasible, comply with state and input constraints, and avoid obstacles in real-time. Further, previous planning algorithms abstract away the obstacle detection problem by assuming the instantaneous availability of geometric information about the environment. In contrast, our method addresses the challenge of using the raw sensor data to form a map and navigate in real-time. Finally, in addition to simulation examples, we provide physical experiments that demonstrate the entire algorithmic pipeline from obstacle detection to trajectory execution.

The combination of on-board sensors measurements with different statistical characteristics can be employed in robotics for localization and control, especially in GPS-denied environments. In particular, most aerial vehicles are packaged with low cost sensors, important for aerial robotics, such as camera, a gyroscope, and an accelerometer. In this work, we develop a visual inertial odometry system based on the Unscented Kalman Filter (UKF) acting on the Lie group SE(3), such to obtain an unique, singularity-free representation of a rigid body pose. We model this pose with the Lie group SE(3) and model the noise on the corresponding Lie algebra. Moreover, we extend the concepts used in the standard UKF formulation, such as state uncertainty and modeling, to correctly incorporate elements that do not belong to an Euclidean space such as the Lie group members. In this analysis, we use the parallel transport, which requires us to explicitly consider SE(3) as representing rigid bodies though the use of the affine connection. We present experimental results to show the effectiveness of the proposed approach for state estimation of a quadrotor platform.

We propose a new optimal trajectory generation technique on SE(3) which avoids known obstacles. We leverage techniques from differential geometry and Lie algebra to formulate a cost functional which is intrinsic to the geometric structure of this space and makes physical sense. We propose an approximation technique to generate trajectories on the subgroup SO(3) and use Semidefinite Programming (SDP) to approximate an NP-Hard problem with one which is tractable to compute. From this trajectory on the subgroup, the trajectory generation on the other dimensions of the group becomes a Quadratic Program (QP). For obstacle avoidance, we use a computational geometric technique to decompose the environment into overlapping convex regions to confine the trajectory. We show how this motion planning technique can be used to generate feasible trajectories for a space robot in SE(3) and describe controllers that enable the execution of the generated trajectory. We compare our method to other geometric techniques for calculating trajectories on SO(3) and SE(3), but in an obstacle-free environment.

Micro Aerial Vehicles (MAVs) are becoming ubiquitous, but most experiments and demonstrations have been limited to slow flight except in open environments or in laboratories with motion capture systems. In this paper, we develop representations and algorithms for aggressive flight in cluttered environments. We incorporate the specific dynamics of the system and generate safe, feasible trajectories for fast navigation in real time. Specifically, we use a polyhedral decomposition of the visible free space and address the generation of safe trajectories that are within the space. Because of the limited field of view, we adopt a receding horizon control policy (RHCP) for planning over a finite time horizon, but with the guarantee that there exists a safe stopping control policy over a second time horizon from the planned state at the end of the horizon. Thus, the robot planning occurs over two horizons. While the robot executes the planned trajectory over the first time horizon, the map of obstacles is refreshed allowing the planner to generate a refined plan, once again over two time horizons. The key algorithmic contribution of the paper lies in the fast planning algorithm that is able to incorporate robot dynamics while guaranteeing safety. The algorithm is also optimal in the sense that the receding horizon control policy is based on minimizing the trajectory snap. Central to the algorithm is a novel polyhedral representation that allows us to abstract the trajectory planning problem as a problem of finding a path through a sequence of convex regions in configuration space.