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Differentiable Factor Graph Optimization for Learning Smoothers

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Figure describing the overall training pipeline proposed by our IROS paper. Contains five sections, arranged left to right: (1) system models, (2) factor graphs for state estimation, (3) MAP inference, (4) state estimates, and (5) errors with respect to ground-truth. Arrows show how gradients are backpropagated from right to left, starting directly from the final stage (error with respect to ground-truth) back to parameters of the system models.

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Overview

Code release for our IROS 2021 conference paper:

<table><tr><td> Brent Yi<sup>1</sup>, Michelle A. Lee<sup>1</sup>, Alina Kloss<sup>2</sup>, Roberto Mart&iacute;n-Mart&iacute;n<sup>1</sup>, and Jeannette Bohg<sup>1</sup>. <strong> <a href="https://sites.google.com/view/diffsmoothing"> Differentiable Factor Graph Optimization for Learning Smoothers. </a> </strong> Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2021. </td></tr></table>

<sup>1</sup><em>Stanford University, {brentyi,michellelee,robertom,bohg}@cs.stanford.edu</em><br /> <sup>2</sup><em>Max Planck Institute for Intelligent Systems, akloss@tue.mpg.de</em>

Bibtex:

@inproceedings{yi2021iros,
    author={Brent Yi and Michelle Lee and Alina Kloss and Roberto Mart\'in-Mart\'in and Jeannette Bohg},
    title = {Differentiable Factor Graph Optimization for Learning Smoothers},
    year = 2021,
    BOOKTITLE = {2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}
}

This repository contains models, training scripts, and experimental results, and can be used to either reproduce our results or as a reference for implementation details.

Significant chunks of the code written for this paper have been factored out of this repository and released as standalone libraries, which may be useful for building on our work. You can find each of them linked here:

Status

Included in this repo for the disk task:

And, for the visual odometry task:

Note that **/ indicates a recursive glob in zsh. This can be emulated in bash>4 via the globstar option (shopt -q globstar).

We've done our best to make our research code easy to parse, but it's still being iterated on! If you have questions, suggestions, or any general comments, please reach out or file an issue.

Setup

We use Python 3.8 and miniconda for development.

  1. Any calls to CHOLMOD (via scikit-sparse, sometimes used for eval but never for training itself) will require SuiteSparse:

    # Mac
    brew install suite-sparse
    
    # Debian
    sudo apt-get install -y libsuitesparse-dev
    
  2. Dependencies can be installed via pip:

    pip install -r requirements.txt
    

    In addition to JAX and the first-party dependencies listed above, note that this also includes various other helpers:

    • torch's Dataset and DataLoader interfaces are used for training.
    • fannypack contains some utilities for working with hdf5 files.

The requirements.txt provided will install the CPU version of JAX by default. For CUDA support, please see instructions from the JAX team.

Datasets

Datasets synced from Google Drive and loaded via h5py automatically as needed. If you're interested in downloading them manually, see lib/kitti/data_loading.py and lib/disk/data_loading.py.

Training

The naming convention for training scripts is as follows: train_{task}_{model type}.py.

All of the training scripts provide a command-line interface for configuring experiment details and hyperparameters. The --help flag will summarize these settings and their default values. For example, to run the training script for factor graphs on the disk task, try:

> python train_disk_fg.py --help

Factor graph training script for disk task.

optional arguments:
  -h, --help            show this help message and exit
  --experiment-identifier EXPERIMENT_IDENTIFIER
                        (default: disk/fg/default_experiment/fold_{dataset_fold})
  --random-seed RANDOM_SEED
                        (default: 94305)
  --dataset-fold {0,1,2,3,4,5,6,7,8,9}
                        (default: 0)
  --batch-size BATCH_SIZE
                        (default: 32)
  --train-sequence-length TRAIN_SEQUENCE_LENGTH
                        (default: 20)
  --num-epochs NUM_EPOCHS
                        (default: 30)
  --learning-rate LEARNING_RATE
                        (default: 0.0001)
  --warmup-steps WARMUP_STEPS
                        (default: 50)
  --max-gradient-norm MAX_GRADIENT_NORM
                        (default: 10.0)
  --noise-model {CONSTANT,HETEROSCEDASTIC}
                        (default: CONSTANT)
  --loss {JOINT_NLL,SURROGATE_LOSS}
                        (default: SURROGATE_LOSS)
  --pretrained-virtual-sensor-identifier PRETRAINED_VIRTUAL_SENSOR_IDENTIFIER
                        (default: disk/pretrain_virtual_sensor/fold_{dataset_fold})

When run, train scripts serialize experiment configurations to an experiment_config.yaml file. You can find hyperparameters in the experiments/ directory for all results presented in our paper.

Evaluation

All evaluation metrics are recorded at train time. The cross_validate.py script can be used to compute metrics across folds:

# Summarize all experiments with means and standard errors of recorded metrics.
python cross_validate.py

# Include statistics for every fold -- this is much more data!
python cross_validate.py --disaggregate

# We can also glob for a partial set of experiments; for example, all of the
# disk experiments.
# Note that the ** wildcard may fail in bash; see above for a fix.
python cross_validate.py --experiment-paths ./experiments/disk/**/

Acknowledgements

We'd like to thank Rika Antonova, Kevin Zakka, Nick Heppert, Angelina Wang, and Philipp Wu for discussions and feedback on both our paper and codebase. Our software design also benefits from ideas from several open-source projects, including Sophus, GTSAM, Ceres Solver, minisam, and SwiftFusion.

This work is partially supported by the Toyota Research Institute (TRI) and Google. This article solely reflects the opinions and conclusions of its authors and not TRI, Google, or any entity associated with TRI or Google.