Autonomous Flight in GPS-Denied Environments Using Monocular Vision and Inertial Sensors

2010 ◽  
Author(s):  
Allen Wu ◽  
Eric Johnson
Keyword(s):  
Inertial Sensors ◽  
Monocular Vision ◽  
Autonomous Flight

Autonomous Flight in GPS-Denied Environments Using Monocular Vision and Inertial Sensors

2013 ◽  
Vol 10 (4) ◽  
pp. 172-186 ◽  
Author(s):  
Allen D. Wu ◽  
Eric N. Johnson ◽  
Michael Kaess ◽  
Frank Dellaert ◽  
Girish Chowdhary
Keyword(s):  
Inertial Sensors ◽  
Monocular Vision ◽  
Autonomous Flight

MEMS-SINS/GPS/Magnetometer Integrated Navigation System for Small Unmanned Aerial Vehicles

2014 ◽  
Vol 568-570 ◽  
pp. 976-986 ◽  
Author(s):  
Cun Xiao Miao ◽  
Juan Juan Cao ◽  
Yang Bin Ou
Keyword(s):  
Low Power ◽  
Real Time ◽  
Navigation System ◽  
Inertial Sensors ◽  
Unscented Kalman Filter ◽  
Flight Test ◽  
Integrated Navigation ◽  
Autonomous Flight ◽  
Integrated Navigation System ◽  
Small Uav

The constraints of weight, volume and power for Small unmanned air vehicle (UAV) restrict the application of sensors with heavy and good performance and powerful processors. This paper presents a real-time solution of autonomous flight navigation and its results for small UAV by applying small, cheap, low precision and low-power integrated navigation system, which includes Strap-down Inertial Navigation System (SINS) based on Micro-electro-mechanical system (MEMS) inertial sensors, Global Positioning System (GPS) receiver and magnetometer. The Square-Root Unscented Kalman filter (SR-UKF) for data fusion using in this MEMS-SINS/GPS/ magnetometer integrated navigation system provides continuous and reliable navigation results for the loops of guidance and control for the small UAV with autonomous flight. The whole integrated navigation system algorithm is implemented within low-power embedded microprocessors. The real-time flight test results show that the MEMS-SINS/GPS/magnetometer integrated navigation system is effective and accurate.

Immunity-Based Framework for Autonomous Flight in GNSS-Denied Environment

2019 ◽  
Vol 12 (6) ◽  
pp. 239
Author(s):  
Mohanad Alnuaimi ◽  
Mario George Perhinschi ◽  
Ghassan Al-Sinbol
Keyword(s):  
Autonomous Flight

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

A Preliminary Framework and Feasibility Study for Spacecraft Disturbance Source Detection and Isolation with Distributed Inertial Sensors

2021 ◽  
Author(s):  
Adam Augustyniak ◽  
David J. Hanley ◽  
Timothy W. Bretl ◽  
Neil J. Hejmanowski ◽  
David L. Carroll
Keyword(s):  
Feasibility Study ◽  
Inertial Sensors ◽  
Source Detection ◽  
Disturbance Source
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