Navigation, Guidance, Control and Dynamics Commons^™

Robust State Estimation Methods For Robotics Applications, Shounak Das

Graduate Theses, Dissertations, and Problem Reports

State estimation is an integral component of any autonomous robotic system. Finding the correct position, velocity, and orientation of an agent in its environment enables it to do other tasks like mapping and interacting with the environment, and collaborating with other agents. State estimation is achieved by using data obtained from multiple sensors and fusing them in a probabilistic framework. These include inertial data from Inertial Measurement Unit (IMU), images from camera, range data from lidars, and positioning data from Global Navigation Satellite Systems (GNSS) receivers. The main challenge faced in sensor-based state estimation is the presence of noisy, erroneous, …

Planning Algorithms Under Uncertainty For A Team Of A Uav And A Ugv For Underground Exploration, Matteo De Petrillo

Graduate Theses, Dissertations, and Problem Reports

Robots’ autonomy has been studied for decades in different environments, but only recently, thanks to the advance in technology and interests, robots for underground exploration gained more attention. Due to the many challenges that any robot must face in such harsh environments, this remains an challenging and complex problem to solve.

As technology became cheaper and more accessible, the use of robots for underground ex- ploration increased. One of the main challenges is concerned with robot localization, which is not easily provided by any Global Navigation Services System (GNSS). Many developments have been achieved for indoor mobile ground robots, making …

Localization Algorithms For Gnss-Denied And Challenging Environments, Chizhao Yang

Graduate Theses, Dissertations, and Problem Reports

In this dissertation, the problem about localization in GNSS-denied and challenging environments is addressed. Specifically, the challenging environments discussed in this dissertation include two different types, environments including only low-resolution features and environments containing moving objects. To achieve accurate pose estimates, the errors are always bounded through matching observations from sensors with surrounding environments. These challenging environments, unfortunately, would bring troubles into matching related methods, such as "fingerprint" matching, and ICP. For instance, in environments with low-resolution features, the on-board sensor measurements could match to multiple positions on a map, which creates ambiguity; in environments with moving objects included, the …

Planetary Rover Inertial Navigation Applications: Pseudo Measurements And Wheel Terrain Interactions, Cagri Kilic

Graduate Theses, Dissertations, and Problem Reports

Accurate localization is a critical component of any robotic system. During planetary missions, these systems are often limited by energy sources and slow spacecraft computers. Using proprioceptive localization (e.g., using an inertial measurement unit and wheel encoders) without external aiding is insufficient for accurate localization. This is mainly due to the integrated and unbounded errors of the inertial navigation solutions and the drifted position information from wheel encoders caused by wheel slippage. For this reason, planetary rovers often utilize exteroceptive (e.g., vision-based) sensors. On the one hand, localization with proprioceptive sensors is straightforward, computationally efficient, and continuous. On the other …

Uncertainty Estimation For Stereo Visual Odometry, Derek W. Ross

Graduate Theses, Dissertations, and Problem Reports

Over the past few decades, unmanned aerial vehicles (UAVs) have been increasingly popular for use in locations that are lacking, or have unreliable global navigation satellite system (GNSS) availability. One of the more popular localization techniques for quadrotors is the use of visual odometry (VO) through monocular, RGB-D, or stereo cameras. With primary applications in the context of Simultaneous Localization And Mapping (SLAM) and indoor navigation, VO is largely used in combination with other sensors through Bayesian filters, namely Extended Kalman Filter (EKF) or Particle Filter. This work investigates the accuracy of two standard covariance estimation techniques for a feature-based …

Uav 6dof Simulation And Kalman Filter For Localizing Radioactive Sources, John G. Goulet

Electronic Theses and Dissertations

Unmanned Aerial Vehicles (UAVs) expand the available mission-space for a wide range of budgets. Using MATLAB, this project has developed a six degree of freedom (6DOF) simulation of UAV flight, an Extended Kalman Filter (EKF), and an algorithm for localizing radioactive sources using low-cost hardware. The EKF uses simulated low-cost instruments in an effort to estimate the UAV state throughout simulated flight.

The 6DOF simulates aerodynamics, physics, and controls throughout the flight and provides outputs for each time step. Additionally, the 6DOF simulation offers the ability to control UAV flight via preset waypoints or in realtime via keyboard input.

Using …

Convolutional Neural Network Architecture Study For Aerial Visual Localization, Jedediah M. Berhold

Theses and Dissertations

In unmanned aerial navigation the ability to determine the aircraft's location is essential for safe flight. The Global Positioning System (GPS) is the default modern application used for geospatial location determination. GPS is extremely robust, very accurate, and has essentially solved aerial localization. Unfortunately, the signals from all Global Navigation Satellite Systems (GNSS) to include GPS can be jammed or spoofed. To this response it is essential to develop alternative systems that could be used to supplement navigation systems, in the event of a lost GNSS signal. Public and governmental satellites have provided large amounts of high-resolution satellite imagery. These …

Reducing Magneto-Inductive Positioning Errors In A Metal-Rich Indoor Environment, Orfeas Kypris, Traian Abrudan, Andrew Markham

Orfeas Kypris

Ferrous objects distort magnetic fields and can significantly increase magneto-inductive positioning errors in indoor environments. In this work, we use image theory in order to formulate an analytical channel model for the magnetic field of a quasi-static magnetic dipole positioned above a perfectly conducting half-space. The proposed model can be used to compensate for the distorting effects that metallic reinforcement bars (rebars) impose on the magnetic field of a magneto-inductive transmitter node in an indoor environment. Good agreement is observed between the analytical solution and numerical solutions obtained from 2-D finite element simulations when the transmitter node is located more …

Localization And System Identification Of A Quadcopter Uav, Kenneth Befus

Masters Theses

The research conducted explores the comparison of several trilateration algorithms as they apply to the localization of a quadcopter micro air vehicle (MAV). A localization system is developed employing a network of combined ultrasonic/radio frequency sensors used to wirelessly provide range (distance) measurements defining the location of the quadcopter in 3-dimensional space. A Monte Carlo simulation is conducted using the extrinsic parameters of the localization system to evaluate the adequacy of each trilateration method as it applies to this specific quadcopter application. The optimal position calculation method is determined.

Furthermore, flight testing is performed in which real range measurement data …