attitude_estimator | A C++ implementation of a nonlinear 3D IMU fusion algorithm
kandi X-RAY | attitude_estimator Summary
kandi X-RAY | attitude_estimator Summary
attitude_estimator is a C++ library. attitude_estimator has no bugs, it has no vulnerabilities and it has low support. However attitude_estimator has a Non-SPDX License. You can download it from GitHub.
##General Overview## Attitude Estimator is a generic platform-independent C++ library that implements an IMU sensor fusion algorithm. Up to 3-axis gyroscope, accelerometer and magnetometer data can be processed into a full 3D quaternion orientation estimate, with the use of a nonlinear Passive Complementary Filter. The library is targeted at robotic applications, but is by no means limited to this. Features of the estimator include gyro bias estimation, transient quick learning, multiple estimation algorithms, tuneable estimator parameters, and near-global stability backed by theoretical analysis. Great emphasis has been placed on having a very efficient, yet totally numerically and algorithmically robust implementation of the filter. The code size has also been kept to a minimum, and has been extremely well-commented. The programmatic interface has also been made as easy as possible. Please refer to the extensive documentation of the library for more information on its capabilities and usage caveats. Attitude Estimator was developed as part of the NimbRo-OP project at the University of Bonn. ##Installation## This library is implemented as a collection of platform-independent C++ source files. To get started just clone the attitude_estimator repository.
##General Overview## Attitude Estimator is a generic platform-independent C++ library that implements an IMU sensor fusion algorithm. Up to 3-axis gyroscope, accelerometer and magnetometer data can be processed into a full 3D quaternion orientation estimate, with the use of a nonlinear Passive Complementary Filter. The library is targeted at robotic applications, but is by no means limited to this. Features of the estimator include gyro bias estimation, transient quick learning, multiple estimation algorithms, tuneable estimator parameters, and near-global stability backed by theoretical analysis. Great emphasis has been placed on having a very efficient, yet totally numerically and algorithmically robust implementation of the filter. The code size has also been kept to a minimum, and has been extremely well-commented. The programmatic interface has also been made as easy as possible. Please refer to the extensive documentation of the library for more information on its capabilities and usage caveats. Attitude Estimator was developed as part of the NimbRo-OP project at the University of Bonn. ##Installation## This library is implemented as a collection of platform-independent C++ source files. To get started just clone the attitude_estimator repository.
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attitude_estimator has a low active ecosystem.
It has 121 star(s) with 47 fork(s). There are 22 watchers for this library.
It had no major release in the last 6 months.
There are 0 open issues and 3 have been closed. On average issues are closed in 5 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of attitude_estimator is current.
Quality
attitude_estimator has no bugs reported.
Security
attitude_estimator has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
attitude_estimator has a Non-SPDX License.
Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
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attitude_estimator releases are not available. You will need to build from source code and install.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of attitude_estimator
attitude_estimator Key Features
No Key Features are available at this moment for attitude_estimator.
attitude_estimator Examples and Code Snippets
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No vulnerabilities reported
Install attitude_estimator
You can download it from GitHub.
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