soul0knight / safe_and_smooth

Code associated to the paper "Safe and Smooth: Certified Continuous-Time Range-Only Localization"

Safe and Smooth: Certified Continuous-Time Range-Only Localization

This repository contains the code to reproduce all results of the paper:

Dümbgen, Frederike, Connor Holmes, and Timothy D. Barfoot. “Safe and Smooth: 
Certified Continuous-Time Range-Only Localization.”, arXiv:2209.04266 [cs.RO], Nov. 2022

A pre-print is available at https://arxiv.org/abs/2209.04266.

Installation

This code was written for Ubuntu 20.04.5, using Python 3.8.10.

Local install

All requirements can be installed by running

pip install -r requirements.txt

To check that the installation is successful, run

pytest .

Docker install

Alternatively, the provided Dockerfile can be used to avoid locally installing dependencies. To build the container, run

sudo docker build -t safe .

To check that the installation is successful, run

sudo docker run -it --volume $(pwd):/safe safe pytest .

Please report any installation issues.

Generate results

There are three types of results reported in the paper:

• Noise study: Run simulate_noise.py to generate the simulation study (Figures 4 and 7 (appendix)).
• Timing study: Run simulate_time.py to generate the runtime comparison (Figure 5)
• Real data: Run evaluate_data.py to evaluate the real dataset (Figures 1, 5 and 6).

If you are using Docker, you can generate all results by running

_scripts/generate_all_results.sh

After generating, all data can be evaluated, and new figures created, using the jupyter notebook SafeAndSmooth.ipynb. For more evaluations of the real dataset, refer to the notebook DatasetEvaluation.ipynb.

Code references

The code refers to the following papers:

• [1] Dümbgen, Frederike, Connor Holmes, and Timothy D. Barfoot. “Safe and Smooth: Certified Continuous-Time Range-Only Localization.”, arXiv:2209.04266 [cs.RO], Nov. 2022
• [2] Barfoot, Tim, Chi Hay Tong, and Simo Sarkka. “Batch Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression,” 2014. https://doi.org/10.15607/RSS.2014.X.001.
• [3] Barfoot, Timothy D. State Estimation for Robotics. Cambridge University Press, 2017. https://doi.org/10.1017/9781316671528.