Algorithm for estimating the orientation of an object in 3D space, through the optimal fusion of gyroscope and accelerometer information

Contreras-Rodriguez, L. A.; Muñoz, Robert; Barraza-Madrigal, José A.

doi:10.1109/iceee.2017.8108879

Cited by 3 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Still, in many applications, the long-term mean of the linear acceleration can be considered negligible concerning the gravity acceleration. The estimated vertical direction from the accelerometer can be combined with the gyroscope readings using various sensor fusion methods, improving the precision of the estimated angular velocity and Euler angles (see, e.g., [ 8 , 9 , 15 , 16 , 17 , 18 , 19 ]). Using multiple sensors also improves the safety of the whole system (see, e.g., [ 20 , 21 ]).…”

Section: Introductionmentioning

confidence: 99%

Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors

Nemec

Andel

Šimák

et al. 2023

Sensors

View full text Add to dashboard Cite

The article deals with sensor fusion and real-time calibration in a homogeneous inertial sensor array. The proposed method allows for both estimating the sensors’ calibration constants (i.e., gain and bias) in real-time and automatically suppressing degraded sensors while keeping the overall precision of the estimation. The weight of the sensor is adaptively adjusted according to the RMSE concerning the weighted average of all sensors. The estimated angular velocity was compared with a reference (ground truth) value obtained using a tactical-grade fiber-optic gyroscope. We have experimented with low-cost MEMS gyroscopes, but the proposed method can be applied to basically any sensor array.

show abstract

Section: Introductionmentioning

confidence: 99%

Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors

Nemec

Andel

Šimák

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Generally, the method of obtaining orientation information through point cloud application 14 and additional sensor fusion 15 was considered, and the object detection model was only applied as a supplementary role. The above technologies require specialized technology and computing cost that cannot be compared to the use of a deep learning-based object detection model alone.…”

Section: Introductionmentioning

confidence: 99%

Mechanical parts picking through geometric properties determination using deep learning

Kim

2022

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

In this study, a system for automatically picking mechanical parts required in the industrial automation field was proposed. In particular, using deep learning, bolts and nuts were recognized and geometric information of these parts was extracted. By applying YOLOv3 specialized in high recognition rate and fast processing speed, the recognition of target object, location, and postural information were obtained. The geometric information for the bolt can be obtained by creating two bounding boxes and calculating the orientation vector formed by these center values of two bounding boxes after successfully detecting two individual bounding boxes. Moreover, to obtain more precise geometric information on bolts and nuts, image distortion compensation on the detected object was done after detecting the center value of the bolt and nut through YOLOv3. Based on this result, it was proven that an automatic picking of the mechanical parts using a five-axis robot was successfully implemented.

show abstract

LightGyro: A Batteryless Orientation Measuring Scheme Based on Light Reflection

Guo

Xie

et al. 2024

ACM Trans. Sen. Netw.

View full text Add to dashboard Cite

In industrial production, the orientation of facility components can indicate whether the facility is on a regular operating track. For example, when a component get loose, the orientation variation of the component would exceed the normal range. A common approach for orientation measurement is to attach an inertial measurement unit (IMU) to the target device. However, the IMU requires additional power maintenance. This paper presents LightGyro, a cheap and efficient batteryless scheme to measure the orientation, in which we attach a reflective film to the target device and use a camera to capture the light spot on the reflective film. The basic idea of LightGyro is to extract the light spots in the captured frame and use their pixel coordinates to infer the orientation. It is difficult to recognize a single light spot because the spot lacks distinctive features. To solve the problem, we switch light sources on and off to regulate the appearance of light spots and utilize frame subtraction to extract light spots. The depth of field of light spot is lost in the process of camera projection, which is necessary for the orientation measurement. To address the issue, we propose a light array-based reflection model to extract the depth of field from the relative positions of multiple light spots. To the best of our knowledge, this is the first work to utilize reflection to measure orientation. Experiment results show that the orientation error of LightGyro decreases with the increasing length of the reflection route and the orientation error can achieve less than 1°.

show abstract

Algorithm for estimating the orientation of an object in 3D space, through the optimal fusion of gyroscope and accelerometer information

Cited by 3 publications

References 6 publications

Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors

Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors

Mechanical parts picking through geometric properties determination using deep learning

LightGyro: A Batteryless Orientation Measuring Scheme Based on Light Reflection

Contact Info

Product

Resources

About