将BROAD数据集中的.hdf5文件转为.txt文件，方便c++处理

cd $proj_root/scripts
pip install pywayne pandas numpy matplotlib
python transfer_hdf5_to_txt.py

运行后，会在根目录/data_txt下生成若干.txt文件，方便c++读取。

先生成所需要用到的可执行文件：

#********************生成可执行文件********************
cd $proj_root/src
mkdir -p build
cd build
cmake ..
make -j12
#********************生成可执行文件********************

然后调用可执行文件，生成各种3dof算法的计算结果：

#********************调用可执行文件********************
cd $proj_root/scripts
sh ./run_algorithms.sh
#********************调用可执行文件********************

This repository contains the data files and example code for the BROAD benchmark for inertial orientation estimation.

For more details, see the following publication:

D. Laidig, M. Caruso, A. Cereatti, T. Seel. BROAD—A Benchmark for Robust Inertial Orientation Estimation. Data, 6 (7):1–20, 2021. https://doi.org/10.3390/data6070072

This repository includes the following:

• data_hdf5: IMU measurement data and OMC ground truth for all trials in HDF5 format.
• data_mat: IMU measurement data and OMC ground truth for all trials as Matlab data files.
• example_code: Python example code to calculate error metrics, process the data files with two orientation estimation algorithms, and re-generate the plots shown in the case study of the publication.
• example_code/out: Output generated by the example code (data files and plots).
• animations: Videos that visualize the IMU measurement data and performed motion for all trials.

Note that the data stored in data_hdf5 and data_mat is identical.

The example code is written in Python and requires Python (>=3.7), numpy, scipy, Cython and matplotlib. In order to run the orientation estimation algorithms via Cython, a compatible C++ compiler must be available.

In order to re-create the results presented in the publications,

1. run process_data.py to run the orientation estimation algorithms with a parameter grid (possibly with the --force option if the files in out/ already exists)
2. run create_plots.py to re-create the plots shown in the case study of the publication.

Additionally, the functions provided in broad_utils.py can also be used in other scripts, e.g. to calculate the error metrics defined in the publication.

Note: If you are using the C implementation of Madgwick's orientation estimation algorithm in your own projects, be aware of this issue.

# create virtual environment
python3 -m venv venv
# active the newly created virtual environment
source venv/bin/activate
# install required packages
pip install -r requirements.txt
# run the algorithms with different parameters and calculate errors
./process_data.py -f
# create plots
./create_plots.py

1. Install Anaconda.

2. Download the Visual Studio Build Tools 2019 installer from https://visualstudio.microsoft.com/downloads/ and in the installer, choose to install the C++ build tools.

   Note: You can skip this step if you already have the correct MSVC compiler installed. If the compiler is missing or not set up correctly, there will be a "Unable to find vcvarsall.bat" error message when running process_data.py.

3. Open the Anaconda Prompt and use cd to navigate to the example_code folder

4. Run the following commands:

   # run the algorithms with different parameters and calculate errors
   python process_data.py -f
   # create plots
   python create_plots.py

   Alternatively, you can also run the scripts using Spyder or any other IDE.

The BROAD dataset is available under the terms of the Creative Commons BY 4.0 license.

The provided example code is dual-licensed under the MIT license and under CC-BY-4.0. Note that the example code directory also includes copies of the orientation estimation algorithm code used in the case study, for which no licensing information is provided by the original authors.

Please see the respective LICENSE files for more details.

Daniel Laidig, laidig at control.tu-berlin.de