An Artificial Neural Network Embedded Position and Orientation Determination Algorithm for Low Cost MEMS INS/GPS Integrated Sensors
TLDR
This study proposes an intelligent position and orientation determination scheme that embeds ANN with conventional Rauch-Tung-Striebel (RTS) smoother to improve the overall accuracy of a MEMS INS/GPS integrated system in post-mission mode.Abstract:
Digital mobile mapping, which integrates digital imaging with direct geo-referencing, has developed rapidly over the past fifteen years. Direct geo-referencing is the determination of the time-variable position and orientation parameters for a mobile digital imager. The most common technologies used for this purpose today are satellite positioning using Global Positioning System (GPS) and Inertial Navigation System (INS) using an Inertial Measurement Unit (IMU). They are usually integrated in such a way that the GPS receiver is the main position sensor, while the IMU is the main orientation sensor. The Kalman Filter (KF) is considered as the optimal estimation tool for real-time INS/GPS integrated kinematic position and orientation determination. An intelligent hybrid scheme consisting of an Artificial Neural Network (ANN) and KF has been proposed to overcome the limitations of KF and to improve the performance of the INS/GPS integrated system in previous studies. However, the accuracy requirements of general mobile mapping applications can’t be achieved easily, even by the use of the ANN-KF scheme. Therefore, this study proposes an intelligent position and orientation determination scheme that embeds ANN with conventional Rauch-Tung-Striebel (RTS) smoother to improve the overall accuracy of a MEMS INS/GPS integrated system in post-mission mode. By combining the Micro Electro Mechanical Systems (MEMS) INS/GPS integrated system and the intelligent ANN-RTS smoother scheme proposed in this study, a cheaper but still reasonably accurate position and orientation determination scheme can be anticipated.read more
Citations
More filters
Journal ArticleDOI
Development of a UAV-LiDAR System with Application to Forest Inventory
TL;DR: The development of a low-cost UAV-LiDAR system and an accompanying workflow to produce 3D point clouds and a novel trajectory determination algorithm fusing observations from a GPS receiver, an Inertial Measurement Unit and a High Definition (HD) video camera are presented.
Journal ArticleDOI
Point Cloud Generation from Aerial Image Data Acquired by a Quadrocopter Type Micro Unmanned Aerial Vehicle and a Digital Still Camera
Tomi Rosnell,Eija Honkavaara +1 more
TL;DR: Recommendations for properties of imaging sensor, data collection and processing of UAV image data to ensure accurate point cloud generation are given.
Journal ArticleDOI
A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification
Tuba Kurban,Erkan Beşdok +1 more
TL;DR: A comparison of training algorithms of radial basis function (RBF) neural networks for classification purposes and results show that the use of the ABC algorithm results in better learning than those of others.
Journal ArticleDOI
GPS/INS/Odometer Integrated System Using Fuzzy Neural Network for Land Vehicle Navigation Applications
TL;DR: The results indicate that the proposed method can improve the position, velocity and attitude accuracy of the integrated system, especially the position parameters, over long GPS outages.
Journal ArticleDOI
Attitude Determination Using a MEMS-Based Flight Information Measurement Unit
TL;DR: The results of the attitude determination using an in-house designed low-cost MEMS-based flight information measurement unit are presented and the proposed quaternion-based extended Kalman filter is intuitive, easy to implement, and reliable for long-term high dynamic maneuvers.
References
More filters
Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Book ChapterDOI
Neural Networks for Pattern Recognition
Suresh Kothari,Heekuck Oh +1 more
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
Book
Applied Optimal Estimation
TL;DR: This is the first book on the optimal estimation that places its major emphasis on practical applications, treating the subject more from an engineering than a mathematical orientation, and the theory and practice of optimal estimation is presented.
Book
Strapdown inertial navigation technology
David Titterton,John Weston +1 more
TL;DR: In this paper, the physical principles of inertial navigation, the associated growth of errors and their compensation, and their application in a broad range of applications are discussed, drawing current technological developments and providing an indication of potential future trends.