Automatic navigation and landing of an indoor AR. drone quadrotor using ArUco marker and inertial sensors

Author(s):  
Mohammad Fattahi Sani ◽  
Ghader Karimian
2017 ◽  
Vol 29 (5) ◽  
pp. 856-863 ◽  
Author(s):  
Naoya Takeishi ◽  
◽  
Takehisa Yairi

In the exploration of a small celestial body, it is important to estimate the position and attitude of the spacecraft, as well as the geometric properties of the target celestial body. In this paper, we propose a method to concurrently estimate these quantities in a highly automatic manner when measurements from an attitude sensor, inertial sensors, and a monocular camera are given. The proposed method is based on the incremental optimization technique, which works with models for sensor fusion, and a tailored initialization scheme developed to compensate for the absence of range sensors. Moreover, we discuss the challenges in developing a fully automatic navigation framework.**This paper is an extended version of a preliminary conference report [1].


2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang

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.


2021 ◽  
Author(s):  
Adam Augustyniak ◽  
David J. Hanley ◽  
Timothy W. Bretl ◽  
Neil J. Hejmanowski ◽  
David L. Carroll

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5242
Author(s):  
Jolene Ziyuan Lim ◽  
Alexiaa Sim ◽  
Pui Wah Kong

The aim of this review is to investigate the common wearable devices currently used in field hockey competitions, and to understand the hockey-specific parameters these devices measure. A systematic search was conducted by using three electronic databases and search terms that included field hockey, wearables, accelerometers, inertial sensors, global positioning system (GPS), heart rate monitors, load, performance analysis, player activity profiles, and competitions from the earliest record. The review included 39 studies that used wearable devices during competitions. GPS units were found to be the most common wearable in elite field hockey competitions, followed by heart rate monitors. Wearables in field hockey are mostly used to measure player activity profiles and physiological demands. Inconsistencies in sampling rates and performance bands make comparisons between studies challenging. Nonetheless, this review demonstrated that wearable devices are being used for various applications in field hockey. Researchers, engineers, coaches, and sport scientists can consider using GPS units of higher sampling rates, as well as including additional variables such as skin temperatures and injury associations, to provide a more thorough evaluation of players’ physical and physiological performances. Future work should include goalkeepers and non-elite players who are less studied in the current literature.


Micromachines ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 402
Author(s):  
Ning Liu ◽  
Tianqi Tian ◽  
Zhong Su ◽  
Wenhao Qi

This paper studies the measurement of motion parameters of a parachute scanning platform. The movement of a parachute scanning platform has fast rotational velocity and a complex attitude. Therefore, traditional measurement methods cannot measure the motion parameters accurately, and thus fail to satisfy the requirements for the measurement of parachute scanning platform motion parameters. In order to solve these problems, a method for measuring the motion parameters of a parachute scanning platform based on a combination of magnetic and inertial sensors is proposed in this paper. First, scanning motion characteristics of a parachute-terminal-sensitive projectile are analyzed. Next, a high-precision parachute scanning platform attitude measurement device is designed to obtain the data of magnetic and inertial sensors. Then the extended Kalman filter is used to filter and observe errors. The scanning angle, the scanning angle velocity, the falling velocity, and the 2D scanning attitude are obtained. Finally, the accuracy and feasibility of the algorithm are analyzed and validated by MATLAB simulation, semi-physical simulation, and airdrop experiments. The presented research results can provide helpful references for the design and analysis of parachute scanning platforms, which can reduce development time and cost.


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