this code is fork from website. This code is a supplementary material to the paper "TLIO: Tight Learned Inertial Odometry". To use the code here requires the user to generate its own dataset and retrain. For more information about the paper and the video materials, please refer to our website.
Dependencies tree can be retrieved from pyproject.toml.
It is written for the poetry tool.
All dependencies can thus be installed at once in a new virtual environment with:
cd src
poetry installThen the virtual environment is accessible with:
poetry shellAlternatively, the dependencies are also specified and can be installed through requirements.txt. First create a virtual environment with python3 interpreter, then run
pip install -r requirements.txt
Next commands should be run from this environment.
A dataset is needed in the format of hdf5 to run with this code. The dataset tree structure looks like this under root directory Dataset:
Dataset
├── test.txt
├── train.txt
├── val.txt
├── seq1
│ ├── atttitude.txt
│ ├── calib_state.txt
│ ├── evolving_state.txt
│ └── data.hdf5
├── seq22
│ ├── atttitude.txt
│ ├── calib_state.txt
│ ├── evolving_state.txt
│ └── data.hdf5
...
data.hdf5 contains raw and calibrated IMU data and processed ground truth data. It is used for both the network and the filter. calib_state.txt contains calibration states from VIO and is used for filter initialization. atttitude.txt and evolving_state.txt are the outputs from AHRS attitude filter and VIO pose estimates. These are not used by the filter, but loaded for comparison / debug purposes.
The generation of data.hdf5 is specified in gen_fb_data.py, which requires interpolated stamped IMU measurement files and time-aligned VIO states files. The user can generate his/her own dataset with a different procedure to obtain the same fields to be used for network training and filter inputs.
test.txt, train.txt and val.txt are list files specifying the split with one sequence name per row in the testing, training and validation datasets respectively. The name should be the same of the sequence directory for example seq1 and seq22 as above.
Timestamps (t) are in microseconds (us). Each row corresponds to data in a single timestamp. All data is delimited by commas.
my_timestamps_p.txtVIO timestamps.- [t]
- Note: single column, skipped first 20 frames
imu_measurements.txtraw and calibrated IMU data- [t, acc_raw (3), acc_cal (3), gyr_raw (3), gyr_cal (3), has_vio]
- Note: calibration through VIO calibration states. The data has been interpolated evenly between images around 1000Hz. Every timestamp in my_timestamps_p.txt will have a corresponding timestamp in this file (has_vio==1).
evolving_state.txtground truth (VIO) states at IMU rate.- [t, q_wxyz (4), p (3), v (3)]
- Note: VIO state estimates with IMU integration. Timestamps are from raw IMU measurements.
calib_state.txtVIO calibration states at image rate (used indata_io.py)- [t, acc_scale_inv (9), gyr_scale_inv (9), gyro_g_sense (9), b_acc (3), b_gyr (3)]
- Note: Changing calibration states from VIO.
atttitude.txtAHRS attitude from IMU- [t, qw, qx, qy, qz]
There are three different modes for the network part.--mode parameter defines the behaviour of main_net.py. Select between train, test and eval.
train: training a network model with training and validation dataset.
test: running an existing network model on testing dataset to obtain concatenated trajectories and metrics.
eval: running an exising network model and save all statistics of data samples for network performance evaluation.
Parameters:
--root_dir: dataset root directory. Each subfolder of root directory is a dataset.
--train_list: directory of the txt file with a list of training datasets. It should contain name of subfolder in root.
--val_list: directory of the txt file with a list of validation datasets. \It should contain name of subfolder in root.
--out_dir: training output directory, where checkpoints and logs folders will be created to store trained models and tensorboard logs respectively. A parameters.json file will also be saved.
Example:
python3 src/main_net.py \
--mode train \
--root_dir data/Dataset \
--train_list data/Dataset/train.txt \
--val_list data/Dataset/val.txt \
--out_dir train_outputsParameters:
--test_list: path of the txt file with a list of testing datasets.
--model_path: path of the trained model to test with.
--out_dir: testing output directory, where a folder for each dataset tested will be created containing estimated trajectory as trajectory.txt and plots if specified. metrics.json contains the statistics for each dataset.
Example:
python3 src/main_net.py \
--mode test \
--root_dir data/Dataset \
--test_list data/Dataset/test.txt \
--model_path models/resnet/checkpoint_*.pt \
--out_dir test_outputs
Parameters:
--out_dir: evaluation pickle file output directory.
--sample_freq: the frequency of network input data sample tested in Hz.
--out_name: (optional) output pickle file name.
Example:
python3 src/main_net.py \
--mode eval \
--root_dir data/Dataset \
--test_list data/Dataset/test.txt \
--model_path models/resnet/checkpoint_*.pt \
--out_dir eval_outputs \
--sample_freq 5 \
--out_name resnet.pklPlease refer to main_net.py for a full list of parameters.
Batch scripts are under src/batch_analysis module. Execute batch scripts from the src folder.
Batch testing tests a list of datasets using multiple models and for each model save the trajectories, plots and metrics into a separate model folder. Output tree structure looks like this:
batch_test_outputs
├── model1
│ ├── seq1
│ │ ├── trajectory.txt
│ │ └── *.png
│ ├── seq2
...
│ └── metrics.json
├── model2
│ ├── seq1
...
│ └── metrics.json
...
Create an output directory and go to the src folder
mkdir batch_test_outputs
cd srcRun batch tests. --model_globbing is the globbing pattern to find all models to test.
python -m batch_runner.net_test_batch \
--root_dir ../data/Dataset \
--data_list ../data/Dataset/test.txt \
--model_globbing "../models/*/checkpoint_*.pt" \
--out_dir ../batch_test_outputs \To save plots as well, change parameter save_plot to True in main_net.py.
Batch evaluation runs the eval mode for multiple models, with various perturbation settings. Different perturbations result in a separate pickle file under each model folder. Output tree structure:
net_eval_outputs
├── model1
│ ├── d-bias-0.0-0.025-grav-0.0.pkl
│ ├── d-bias-0.0-0.05-grav-0.0.pkl
│ ├── d-bias-0.0-0.075-grav-0.0.pkl
│ ├── d-bias-0.0-0.0-grav-0.0.pkl
│ ├── d-bias-0.0-0.0-grav-10.0.pkl
│ ├── d-bias-0.0-0.0-grav-2.0.pkl
│ ├── d-bias-0.0-0.0-grav-4.0.pkl
│ ├── d-bias-0.0-0.0-grav-6.0.pkl
│ ├── d-bias-0.0-0.0-grav-8.0.pkl
│ ├── d-bias-0.0-0.1-grav-0.0.pkl
│ ├── d-bias-0.1-0.0-grav-0.0.pkl
│ ├── d-bias-0.2-0.0-grav-0.0.pkl
│ ├── d-bias-0.3-0.0-grav-0.0.pkl
│ ├── d-bias-0.4-0.0-grav-0.0.pkl
│ └── d-bias-0.5-0.0-grav-0.0.pkl
├── model2
│ ├── d-bias-0.0-0.025-grav-0.0.pkl
│ ├── d-bias-0.0-0.05-grav-0.0.pkl
...
In the current script, the following perturbation values are used:
Accelerometer bias perturbation range: [0, 0.1, 0.2, 0.3, 0.4, 0.5] (m/s^2)
Gyroscope bias perturbation range: [0, 0.025, 0.05, 0.075, 0.1] (rad/s)
Gravity direction perturbation range: [0, 0, 2, 4, 6, 8, 10] (degrees)
These can be changed in the script batch_runner/net_eval_batch.py, and for each perturbation range a pkl file will be saved with the range in the filename.
Create an output directory and go to the src folder
mkdir batch_eval_outputs
cd srcRun batch evaluation
python -m batch_runner.net_eval_batch \
--root_dir ../data/Dataset \
--data_list ../data/Dataset/test.txt \
--model_globbing "../models/*/checkpoint_*.pt" \
--out_dir ../net_eval_outputs \
--sample_freq 5.0After running testing and evaluation in batches, the statistics are saved in either metrics.json or the generated pickle files. To visualize the results and compare between models, we provide scripts that display the results in an interactive shell through iPython. The scripts are under src/analysis module.
To visualize network testing results from metrics.json including trajectory metrics and testing losses, go to src folder and run
python -m analysis.display_json \
--glob_dataset "../batch_test_output/*/"This will leave you in an interactive shell with a preloaded panda DataFrame d. You can use it to visualize all metrics with the following helper function:
plot_all_stats_net(d)To visualize evaluation results from pickle files, run
python -m analysis.display_pickle \
--glob_pickle "../batch_eval_outputs/*/*.pkl"This gives access to all the sample data 3D displacement gt and errors, sigmas, mse and likelihood losses, 2D norm and angle gt and errors, and mahalanobis distance based on the regressed covariance. To plot sigmas vs. errors for example, run
plot_sigmas(d)Use src/main_filter.py for running the filter and parsing parameters. The program supports running multiple datasets on one specified network model.
Parameters:
--model_path: path to saved model checkpoint file.
--model_param_path: path to parameter json file for this model.
--out_dir: filter output directory. This will include a parameters.json file with filter parameters, and a folder for each dataset containing the logged states, default to not_vio_state.txt.
--erase_old_log: overwrite old log files. If set to --no-erase_old_log, the program would skip running on the datasets if the output file already exists in the output directory.
--save_as_npy: convert the output txt file to npy file and append file extension (e.g. not_vio_state.txt.npy) to save space.
--initialize_with_offline_calib: initialize with offline calibration of the IMU. If set to --no-initialize_with_offline_calib the initial IMU biases will be initialized to 0.
Example:
python3 src/main_filter.py \
--root_dir data/Dataset \
--data_list data/Dataset/test.txt \
--model_path models/resnet/checkpoint_75.pt \
--model_param_path models/resnet/parameters.json \
--out_dir filter_outputs \
--erase_old_log \
--save_as_npy \
--initialize_with_offline_calibPlease refer to main_filter.py for a full list of parameters.
Batch script batch_runner/filter_batch provides functionality to run the main file in batch settings. Go to src folder to run the module and you can set the parameters to test within the script (e.g. different update frequencies).
Example:
python -m batch_runner.run_batch \
--root_dir ../data/Dataset \
--data_list ../data/Dataset/test.txt \
--model_globbing "../models/*/checkpoint_*.pt" \
--out_dir ../batch_filter_outputsTo generate plots of the states of the filter and to generate metrics.json file for both the filter and network concatenation approaches, batch run plot_state.py on the existing filter and network testing outputs.
Parameters:
--runname_globbing: globbing pattern for all the model names to plot. This pattern should match between filter and ronin and exist in both --filter_dir and --ronin_dir.
--no_make_plots: not to save plots. If removed plots will be saved in the filter output folders for each trajectory.
Example:
python -m batch_runner.plot_batch \
--root_dir ../data/Dataset \
--data_list ../data/Dataset/test.txt \
--runname_globbing "*" \
--filter_dir ../batch_filter_outputs \
--ronin_dir ../batch_test_outputs \Up to now a metrics.json file will be added to each model folder, and the tree structure would look like this:
batch_filter_outputs
├── model1
│ ├── seq1
│ │ ├── *.png
│ │ ├── not_vio_state.txt.npy
│ │ └── vio_states.npy
│ ├── seq1
│ │ ├── *.png
│ │ ├── not_vio_state.txt.npy
│ │ └── vio_states.npy
...
│ ├── metrics.json
│ └── parameters.json
├── model2
...
To generate plots from the metrics:
python -m analysis.display_json \
--glob_dataset "../batch_filter_outputs/*/"