GPS denied environments cause each unmanned ground vehicle (UGV) in an autonomous convoy to lose positional accuracy which can lead to inoperability, or even damage. In order for autonomous convoy systems to fill the needs of any particular field, a well-performing system must be designed such that a convoy can operate in any environment. Ultra-wideband (UWB) radios are a proposed solution to GPS denied localization.The main objective of this research is to use UWB radios to localize a leader vehicle within a convoy situation while in a GPS denied environment.

Unmanned vehicles are increasingly common in our world today. Self-driving ground vehicles and unmanned aerial vehicles (UAVs) such as quadcopters have become the fastest growing area of automated vehicles research. These systems use three main processes to autonomously travel from one location to another: guidance, navigation, and controls (GNC). Guidance refers to the process of determining a desired path of travel or trajectory, affecting velocities and orientations. Examples of guidance activities include path planning and obstacle avoidance. Effective guidance decisions require knowledge of one’s current location. Navigation systems typically answer questions such as: “Where am I? What is my orientation? How fast am I going?” Finally, the process is tied together when controls are implemented. Controls use navigation estimates (e.g., “Where I am now?”) and the desired trajectory from guidance processes (e.g., “Where do I want to be?”) to control the moving parts of the system to accomplish relevant goals. Navigation in autonomous vehicles involves intelligently combining information from several sensors to produce accurate state estimations. To date, global positioning systems(GPS) occupy a crucial place in most navigation systems. However, GPS is not universally reliable. Even when available, GPS can be easily spoofed or jammed, rendering it useless. Thus, navigation within GPS-denied environments is an area of deep interest in both military and civilian applications. Image-aided inertial navigation is an alternative navigational solution in GPS-denied environments. One form of image-aided navigation measures the bearing from the vehicle to a feature or landmark of known location using a single lens imager, such as a camera, to deduce information about the vehicle’s position and attitude. This work uncovers and explores several of the impacts of trajectories and land mark distributions on the navigation information gained from this type of aiding measurement. To do so, a modular system model and extended Kalman filter (EKF) are described and implemented. A quadrotor system model is first presented. This model is implemented and then used to produce sensor data for several trajectories of varying shape, altitude, and landmark density. Next, navigation data is produced by running the sensor data through an EKF. The data is plotted and examined to determine effects of each variable. These effects are then explained. Finally, an equation describing the quantity of information in each measurement is derived and related to the patterns seen in the data. The resulting equation is then used to explain selected patterns in the data. Other uses of this equation are presented, including applications to path planning and landmark placement.

Unmanned aerial systems (UAS) are effective for surveillance and monitoring, but struggle with persistent, long-term tracking, especially without GPS, due to limited flight time. Persistent tracking can be accomplished using multiple vehicles if one vehicle can effectively hand off the tracking information to another replacement vehicle. This work presents a solution to the moving-target handoff problem in the absence of GPS. The proposed solution (a) a nonlinear complementary filter for self-pose estimation using only an IMU, (b) a particle filter for relative pose estimation between UAS using a relative range (c) visual target tracking using a gimballed camera when the target is close to the handoff UAS, and (d) track correlation logic using Procrustes analysis to perform the final target handoff between vehicles. We present hardware results of the self-pose estimation and visual target tracking, as well as an extensive simulation result that demonstrates the effectiveness of our full system, and perform Monte-Carlo simulations that indicate a 97% successful handoff rate using the proposed methods.

This thesis discusses topics relevant to indoor unmanned quadrotor navigation and control. These topics include: quadrotor modeling, sensor modeling, quadrotor parameter estimation, sensor calibration, quadrotor state estimation using onboard sensors, and cooperative GPS navigation. Modeling the quadrotor, sensor modeling, and parameter estimation are essential components for quadrotor navigation and control. This thesis investigates prior work and organizes a wide variety of models and calibration methods that enable indoor unmanned quadrotor flight. Quadrotor parameter estimation using a particle filter is a contribution that extends current research in the area. This contribution is novel in that it applies the particle filter specifically to quadrotor parameter estimation as opposed to quadrotor state estimation. The advantages and disadvantages of such an approach are explained. Quadrotor state estimation using onboard sensors and without the aid of GPS is also discussed, as well as quadrotor pose estimation using the Extended Kalman Filter with an inertial measurement unit and simulated 3D camera updates. This is done using two measurement updates: one from the inertial measurement unit and one from the simulated 3D camera. Finally, we demonstrate that when GPS lock cannot be obtained by an unmanned vehicle individually. A group of cooperative robots with pose estimates to one anther can exploit partial GPS information to improve global position estimates for individuals in the group. This method is advantageous for robots that need to navigate in environments where signals from GPS satellites are partially obscured or jammed.

Ground robotic vehicles are used in many different applications. Many of these uses include tele-operation of the robot. This allows the robot to be deployed in locations that are too difficult or are unsafe for human access. The ability of a ground robot to autonomously navigate to a desired location without a-priori map information and without using GPS would allow robotic vehicles to be used in many of these situations and would free the operator to focus on other more important tasks. The purpose of this research is to develop algorithms that enable a ground robot to autonomously navigate to a user-selected location. The goal is selected from a video feed from the robot and the robot drives to the goal location while avoiding obstacles. The method uses a monocular camera for measuring the locations of the goal and landmarks. The method is validated in simulation and through experiments on an iRobot Packbot platform. A novel goal-based robocentric mapping algorithm is derived in Chapter 3. This map is created using an extended Kalman filter (EKF) by tracking the position of the goal along with other available landmarks surrounding the robot as it drives towards the goal. The mapping is robocentric, meaning that the map is a local map created in the robot-body frame. A unique state definition of the goal states and additional landmarks is presented that improves the estimate of the goal location. An improved 3D model is derived and used to allow the robot to drive on non-flat terrain while calculating the position of the goal and other landmarks. The observability and consistency of the proposed method are shown in Chapter 4. The visual tracking algorithm is explained in Chapter 5. This tracker is used with the EKF to improve tracking performance and to allow the objects to be tracked even after leaving the camera field of view for significant periods of time. This problem presents a difficult challenge for visual tracking because of the drastic change in size of the goal object as the robot approaches the goal. The tracking method is validated through experiments in real-world scenarios. The method of planning and control is derived in Chapter 6. A Model Predictive Control (MPC) formulation is designed that explicitly handles the sensor constraints of a monocular camera that is rigidly mounted to the vehicle. The MPC uses an observability-based cost function to drive the robot along a path that minimizes the position error of the goal in the robot-body frame. The MPC algorithm also avoids obstacles while driving to the goal. The conditions are explained that guarantee the robot will arrive within some specified distance of the goal. The entire system is implemented on an iRobot Packbot and experiments are conducted and presented in Chapter 7. The methods described in this work are shown to work on actual hardware allowing the robot to arrive at a user-selected goal in real-world scenarios.

abstract: The exponential rise in unmanned aerial vehicles has necessitated the need for accurate pose estimation under any extreme conditions. Visual Odometry (VO) is the estimation of position and orientation of a vehicle based on analysis of a sequence of images captured from a camera mounted on it. VO offers a cheap and relatively accurate alternative to conventional odometry techniques like wheel odometry, inertial measurement systems and global positioning system (GPS). This thesis implements and analyzes the performance of a two camera based VO called Stereo based visual odometry (SVO) in presence of various deterrent factors like shadows, extremely bright outdoors, wet conditions etc... To allow the implementation of VO on any generic vehicle, a discussion on porting of the VO algorithm to android handsets is presented too. The SVO is implemented in three steps. In the first step, a dense disparity map for a scene is computed. To achieve this we utilize sum of absolute differences technique for stereo matching on rectified and pre-filtered stereo frames. Epipolar geometry is used to simplify the matching problem. The second step involves feature detection and temporal matching. Feature detection is carried out by Harris corner detector. These features are matched between two consecutive frames using the Lucas-Kanade feature tracker. The 3D co-ordinates of these matched set of features are computed from the disparity map obtained from the first step and are mapped into each other by a translation and a rotation. The rotation and translation is computed using least squares minimization with the aid of Singular Value Decomposition. Random Sample Consensus (RANSAC) is used for outlier detection. This comprises the third step. The accuracy of the algorithm is quantified based on the final position error, which is the difference between the final position computed by the SVO algorithm and the final ground truth position as obtained from the GPS. The SVO showed an error of around 1% under normal conditions for a path length of 60 m and around 3% in bright conditions for a path length of 130 m. The algorithm suffered in presence of shadows and vibrations, with errors of around 15% and path lengths of 20 m and 100 m respectively. / Dissertation/Thesis / M.S. Electrical Engineering 2010

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The use of multirotor UAVs to map GPS-degraded environments is useful for many purposes ranging from routine structural inspections to post-disaster exploration to search for survivors and evaluate structural integrity. Multirotor UAVs are able to reach many areas that humans and other robots cannot safely access. Because of their relatively short operational flight time compared to other robotic applications, using multiple UAVs to collaboratively map these environments can streamline the mapping process significantly. This research focuses on four primary areas regarding autonomous mapping and navigation with multiple UAVs in complex unknown or partially unknown GPS-denied environments: The first area is the high-level coverage path planning necessary to successfully map these environments with multiple agents. The second area is the lower-level reactive path planning that enables autonomous navigation through complex, unknown environments. Third, is the estimation framework that enables autonomous flight without the use of GPS or other global position sensors. Lastly, it focuses on the mapping framework to build a single dense 3D map of these environments with multiple agents flying simultaneously.

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Can an outward facing camera on a bus, be used to recognize its location in GPS denied environment? Observit, provides cloud-based mobile surveillance services for bus operators using IP cameras with wireless connectivity. With the continuous gathering of video information, it opens up new possibilities for additional services. One service is to use the information with the technology, visual place recognition, to locate the vehicle, where the image was taken. The objective of this thesis has been to answer, how well can learnable visual place recognition methods localize a bus in a GPS denied environment and if a lightweight model can achieve the same accurate results as a heavyweight model. In order to achieve this, four model architecture has been implemented, trained and evaluate on a created dataset of interesting places. A visual place recognition application has been implemented as well, in order to test the models on bus video footage. The results show that the heavyweight model constructed of VGG16 with Patch-NetVLAD, performed best on the task with different recall@N values and got a recall@1 score of 92.31%. The lightweight model that used the backbone of MobileNetV2 with Patch-NetVLAD, scored similar recall@N results as the heavyweight model and got the same recall@1 score. The thesis shows that, with different localization methods, it is possible for a vehicle to identify its position in a GPS denied environment, with a model that could be deploy on a camera. This work, impacts companies that rely on cameras as their source of service.