423 research outputs found
Simple yet stable bearing-only navigation
This article describes a simple monocular navigation system for a mobile robot based on the map-and-replay technique. The presented method is robust and easy to implement and does not require sensor calibration or structured environment, and its computational complexity is independent of the environment size. The method can navigate a robot while sensing only one landmark at a time, making it more robust than other monocular approaches. The aforementioned properties of the method allow even low-cost robots to effectively act in large outdoor and indoor environments with natural landmarks only. The basic idea is to utilize a monocular vision to correct only the robot's heading, leaving distance measurements to the odometry. The heading correction itself can suppress the odometric error and prevent the overall position error from diverging. The influence of a map-based heading estimation and odometric errors on the overall position uncertainty is examined. A claim is stated that for closed polygonal trajectories, the position error of this type of navigation does not diverge. The claim is defended mathematically and experimentally. The method has been experimentally tested in a set of indoor and outdoor experiments, during which the average position errors have been lower than 0.3 m for paths more than 1 km long
Highly efficient Localisation utilising Weightless neural systems
Efficient localisation is a highly desirable property for an autonomous navigation system. Weightless neural networks offer a real-time approach to robotics applications by reducing hardware and software requirements for pattern recognition techniques. Such networks offer the potential for objects, structures, routes and locations to be easily identified and maps constructed from fused limited sensor data as information becomes available. We show that in the absence of concise and complex information, localisation can be obtained using simple algorithms from data with inherent uncertainties using a combination of Genetic Algorithm techniques applied to a Weightless Neural Architecture
System Identification of a Micro Aerial Vehicle
The purpose of this thesis was to implement an Model Predictive Control based system identification method on a micro-aerial vehicle (DJI Matrice 100) as outlined in a study performed by ETH Zurich. Through limited test flights, data was obtained that allowed for the generation of first and second order system models. The first order models were robust, but the second order model fell short due to the fact that the data used for the model was not sufficient
Vision-based localization methods under GPS-denied conditions
This paper reviews vision-based localization methods in GPS-denied
environments and classifies the mainstream methods into Relative Vision
Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss
the broad application of optical flow in feature extraction-based Visual
Odometry (VO) solutions and introduce advanced optical flow estimation methods.
For AVL, we review recent advances in Visual Simultaneous Localization and
Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman
Filter (EKF) based methods. We also introduce the application of offline map
registration and lane vision detection schemes to achieve Absolute Visual
Localization. This paper compares the performance and applications of
mainstream methods for visual localization and provides suggestions for future
studies.Comment: 32 pages, 15 figure
Vision-based SLAM for the aerial robot ErleCopter
El objetivo principal de este trabajo, es la implementación de distintos tipos de algoritmos SLAM (mapeado y localización simultáneos) de visión monocular en el robot aéreo ErleCopter, empleando la plataforma software ROS (Robotic Operating System).
Para ello se han escogido un conjunto de tres algoritmos ampliamente utilizados en el campo de la visión artificial: PTAM, ORB-SLAM y LSD-SLAM. Así se llevará a cabo un estudio del funcionamiento de los mismos en el ErleCopter.
Además empleando dichos algoritmos, y fusionando la información extraída por estos con la información de otros sensores presentes en la plataforma robótica, se realizará un EKF (Extended Kalman Filter), de forma que podamos predecir la localización del robot de una manera más exacta en entornos interiores, ante la ausencia de sistemas GPS.
Para comprobar el funcionamiento del sistema se empleará la plataforma de simulación robótica Gazebo.
Por último se realizarán pruebas con el robot real, de forma que podamos observar y extraer conclusiones del funcionamiento de estos algoritmos sobre el propio ErleCopter.The main objective of this thesis is the implementation of different SLAM (Simultaneous Localization and Mapping) algorithms within the aerial robot ErleCopter, using the software platform ROS (Robotic Operating System).
To do so, a bunch of three widely known and used algorithms in the field of the artificial vision have been chosen: PTAM, ORB-SLAM y LSD-SALM. So a study of the performance of such algorithms will be carried out in this way.
Besides, working with such algorithms and fusing their information with the one obtained by other sensors existing within the robotic platform, an EKF (Extended Kalman Filter) will be carried out, in order to localize the robot more accurately in indoor environments, given the lack of GPS.
To test the performance of the system, the robotic platform Gazebo will be used in this project.
Finally tests will be made with the real robot, in order to observe and draw conclusions from the performance of these algorithms within the ErleCopter itself.Máster Universitario en Ingeniería Industrial (M141
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