Vision-based SLAM system for MAVs in GPS-denied environments

Using a camera, a micro aerial vehicle (MAV) can perform visual-based navigation in periods or circumstances when GPS is not available, or when it is partially available. In this context, the monocular simultaneous localization and mapping (SLAM) methods represent an excellent alternative, due to several limitations regarding to the design of the platform, mobility and payload capacity that impose considerable restrictions on the available computational and sensing resources of the MAV. However, the use of monocular vision introduces some technical difficulties as the impossibility of directly recovering the metric scale of the world. In this work, a novel monocular SLAM system with application to MAVs is proposed. The sensory input is taken from a monocular downward facing camera, an ultrasonic range finder and a barometer. The proposed method is based on the theoretical findings obtained from an observability analysis. Experimental results with real data confirm those theoretical findings and show that the proposed method is capable of providing good results with low-cost hardware.Peer ReviewedPostprint (published version

Autonomous Navigation in Complex Indoor and Outdoor Environments with Micro Aerial Vehicles

Micro aerial vehicles (MAVs) are ideal platforms for surveillance and search and rescue in confined indoor and outdoor environments due to their small size, superior mobility, and hover capability. In such missions, it is essential that the MAV is capable of autonomous flight to minimize operator workload. Despite recent successes in commercialization of GPS-based autonomous MAVs, autonomous navigation in complex and possibly GPS-denied environments gives rise to challenging engineering problems that require an integrated approach to perception, estimation, planning, control, and high level situational awareness. Among these, state estimation is the first and most critical component for autonomous flight, especially because of the inherently fast dynamics of MAVs and the possibly unknown environmental conditions. In this thesis, we present methodologies and system designs, with a focus on state estimation, that enable a light-weight off-the-shelf quadrotor MAV to autonomously navigate complex unknown indoor and outdoor environments using only onboard sensing and computation. We start by developing laser and vision-based state estimation methodologies for indoor autonomous flight. We then investigate fusion from heterogeneous sensors to improve robustness and enable operations in complex indoor and outdoor environments. We further propose estimation algorithms for on-the-fly initialization and online failure recovery. Finally, we present planning, control, and environment coverage strategies for integrated high-level autonomy behaviors. Extensive online experimental results are presented throughout the thesis. We conclude by proposing future research opportunities

Long Distance GNSS-Denied Visual Inertial Navigation for Autonomous Fixed Wing Unmanned Air Vehicles: SO(3) Manifold Filter based on Virtual Vision Sensor

This article proposes a visual inertial navigation algorithm intended to diminish the horizontal position drift experienced by autonomous fixed wing UAVs (Unmanned Air Vehicles) in the absence of GNSS (Global Navigation Satellite System) signals. In addition to accelerometers, gyroscopes, and magnetometers, the proposed navigation filter relies on the accurate incremental displacement outputs generated by a VO (Visual Odometry) system, denoted here as a Virtual Vision Sensor or VVS, which relies on images of the Earth surface taken by an onboard camera and is itself assisted by the filter inertial estimations. Although not a full replacement for a GNSS receiver since its position observations are relative instead of absolute, the proposed system enables major reductions in the GNSS-Denied attitude and position estimation errors. In order to minimize the accumulation of errors in the absence of absolute observations, the filter is implemented in the manifold of rigid body rotations or SO (3). Stochastic high fidelity simulations of two representative scenarios involving the loss of GNSS signals are employed to evaluate the results. The authors release the C++ implementation of both the visual inertial navigation filter and the high fidelity simulation as open-source software.Comment: 27 pages, 14 figures. arXiv admin note: substantial text overlap with arXiv:2205.1324

A virtual odometer for a Quadrotor Micro Aerial Vehicle

This paper describes the synthesis and evaluation of a "virtual odometer" for a Quadrotor Micro Aerial Vehicle. Availability of a velocity estimate has the potential to enhance the accuracy of mapping, estimation and control algorithms used with quadrotors, increasing the effectiveness of their applications. As a result of the unique dynamic characteristics of the quadrotor, a dual axis accelerometer mounted parallel to the propeller plane provides measurements that are directly proportional to vehicle velocities in that plane. Exploiting this insight, we encapsulate quadrotor dynamic equations which relate acceleration, attitude and the aerodynamic propeller drag in an extended Kalman filter framework for the purpose of state estimation. The result is a drift free estimation of lateral and longitudinal components of translational velocity and roll and pitch components of attitude of the quadrotor. Real world data sets gathered from two different quadrotor platforms, together with ground truth data from a Vicon system, are used to evaluate the effectiveness of the proposed algorithm and demonstrate that drift free estimates for the velocity and attitude can be obtained

PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision

We describe a novel quadrotor Micro Air Vehicle (MAV) system that is designed to use computer vision algorithms within the flight control loop. The main contribution is a MAV system that is able to run both the vision-based flight control and stereo-vision-based obstacle detection parallelly on an embedded computer onboard the MAV. The system design features the integration of a powerful onboard computer and the synchronization of IMU-Vision measurements by hardware timestamping which allows tight integration of IMU measurements into the computer vision pipeline. We evaluate the accuracy of marker-based visual pose estimation for flight control and demonstrate marker-based autonomous flight including obstacle detection using stereo vision. We also show the benefits of our IMU-Vision synchronization for egomotion estimation in additional experiments where we use the synchronized measurements for pose estimation using the 2pt+gravity formulation of the PnP proble

Monocular SLAM system for MAVs aided with altitude and range measurements: a GPS-free approach

A typical navigation system for a Micro Aerial Vehicle (MAV) relies basically on GPS for position estimation. However,for several kinds of applications, the precision of the GPS is inappropriate or even its signal can be unavailable. In this context, and due to its flexibility, Monocular Simultaneous Localization and Mapping (SLAM) methods have become a good alternative for implementing visual-based navigation systems for MAVs that must operate in GPS-denied environments. On the other hand, one of the most important challenges that arises with the use of the monocular vision is the difficulty to recover the metric scale of the world. In this work, a monocular SLAM system for MAVs is presented. In order to overcome the problem of the metric scale, a novel technique for inferring the approximate depth of visual features from an ultrasonic range-finder is developed. Additionally, the altitude of the vehicle is updated using the pressure measurements of a barometer. The proposed approach is supported by the theoretical results obtained from a nonlinear observability test. Experiments performed with both computer simulations and real data are presented in order to validate the performance of the proposal. The results confirm the theoretical findings and show that the method is able to work with low-cost sensors.Peer ReviewedPostprint (author's final draft

Development of Cursor-on-Target Control for Semi-Autonomous Unmanned Aircraft Systems

The research presented in this thesis focuses on developing, demonstrating, and evaluating the concept of a Cursor-on-Target control system for semi-autonomous unmanned aircraft systems. The Department of Defense has mapped out a strategy in which unmanned aircraft systems will increasingly replace piloted aircraft. During most phases of flight autonomous unmanned aircraft control reduces operator workload, however, real-time information exchange often requires an operator to relay decision changes to the unmanned aircraft. The goal of this research is to develop a preliminary Cursor-on-Target control system to enable the operator to guide the unmanned aircraft with minimal workload during high task phases of flight and then evaluate the operator\u27s ability to conduct the mission using that control system. For this research, the problem of Cursor-on-Target control design has multiple components. Initially, a Cursor-on-Target controller is developed in Simulink. Then, this controller is integrated into the Aviator Visual Design Simulator to develop an operator-in-the-loop test platform. Finally, a ground target is simulated and tracked to validate the Cursor-on-Target controller. The Cursor-on-Target control system is then evaluated using a proposed operator rating scale