Essays about: "Kalman Filter. Navigation"

Showing result 6 - 10 of 73 essays containing the words Kalman Filter. Navigation.

1. 6. Probabilistic Multi-Modal Data Fusion and Precision Coordination for Autonomous Mobile Systems Navigation : A Predictive and Collaborative Approach to Visual-Inertial Odometry in Distributed Sensor Networks using Edge Nodes

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Isabella Luppi; [2023]
   Keywords : Distributed Sensor Networks; Point Cloud Processing; Bounding Box Fitting; Trajectory Tracking; Distributed Estimation; Predictive Estimation; Edge-Computing; Reti di Sensori Distribuiti; Elaborazione di Nuvole di Punti; Riquadri di Delimitazione; Tracciamento della Traiettoria; Stima Distribuita; Stima Predittiva; Calcolo Distribuito.; Distribuerade Sensornätverk; Bearbetning av Punktmoln; Anpassning av Begränsningsruta; Trajektorieuppföljning; Distribuerad Uppskattning; Prediktiv Uppskattning; Edge-datorbehandling;

   Abstract : This research proposes a novel approach for improving autonomous mobile system navigation in dynamic and potentially occluded environments. The research introduces a tracking framework that combines data from stationary sensing units and on-board sensors, addressing challenges of computational efficiency, reliability, and scalability. READ MORE

3. 7. Enhancing GNSS Precision for Mobile Devices with Sensor Fusion Techniques: A Case Study on eBike Tracking Using State Estimation

   University essay from Lunds universitet/Institutionen för reglerteknik

   Author : Richard Byström; William Sjödin; [2023]
   Keywords : Technology and Engineering;

   Abstract : Electric bicycles have over time become a common method of transportation. With a rapidly increasing user base and expensive prices, there is also an increasing demand for safety and insurance. Bike safety can be used to notify the bicycle owner of a potential theft, or to locate the position of the bike when it is lost. READ MORE

4. 8. A Positioning System for Landing a UAV on a UGV in a GNSS-Denied Scenario

   University essay from Linköpings universitet/Institutionen för systemteknik

   Author : Tim Wiik; [2022]
   Keywords : Sensor fusion; UAV; UGV; NIR-LED; Extended kalman filter; Unmanned Aerial Vehicle; Unmanned Ground Vehicle; Autonomous Landing; GNSS-denied Scenario;

   Abstract : A system of an unmanned aerial vehicle (UAV) collaborating with an unmanned ground vehicle (UGV) for use in for example surveillance, reconnaissance, transport and target acquisition is studied. The project investigates the problem of estimating the relative position, velocity and orientation between the UAV and the UGV required to autonomously land the UAV on the UGV during movement. READ MORE

5. 9. GPS and IMU Sensor Fusion to Improve Velocity Accuracy

   University essay from Uppsala universitet/Institutionen för elektroteknik

   Author : Adam Laurell; Erik Karlsson; Yousuf Naqqar; [2022]
   Keywords : Sensor fusion; GPS navigation; Extended Kalman filter; Control systems; Velocity estimation; Data processing;

   Abstract : The project explores the possibilities on how to improve the accuracy of GPS velocity data by using sensor fusion with an extended Kalman filter. The proposed solution in this project is a sensor fusion between the GPS and IMU of the system, where the extended Kalman filter was used to estimate the velocity from the sensor data. READ MORE

6. 10. Localization For AutonomousDriving using Statistical Filtering : A GPS aided navigation approach with EKF and UKF

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Devrat Singh; [2022]
   Keywords : Land Vehicle Localization; Sensor Fusion; GNSS-INS Fusion; UKF; EKF;

   Abstract : A critical requirement for safe autonomous driving is to have an accurate state estimate of thevehicle. One of the most ubiquitous yet reliable ways for this task is through the integrationof the onboard Inertial Navigation System (INS) and the Global Navigation Satellite System(GNSS). READ MORE