Awesome
LocNDF
LocNDF: Neural Distance Field Mapping for Robot Localization
Installation
The system has been tested with Ubuntu-20.04 and Python3.9. Install the following dependencies if needed:
apt-get install libgl1-mesa-glx libx11-dev ldconfig
For the installation of the python package simply clone the repo and pip install it.
git clone git@github.com:PRBonn/LocNDF.git
cd LocNDF
pip install .
Optionally one can use pip install -e .
for the editable mode if you plan to change the src code.
Usage
The following commands are expected to be executed in this root directory.
Pose tracking in Apollo Southbay
First, get the data:
Download the Apollo Southbay dataset and place it in data/
(or create a symlink). The ColumbiaPark/
set is enough for the examples.
Training one Submap
For training a single model you can configure config/config.yaml
and run scripts_pose_tracking/train.py
.
Registering a scan to the trained model can be done using scripts_pose_tracking/register_scan.py
while only visualizing the meshed result one can use scripts_pose_tracking/mesh_it.py
.
Training multiple key-poses
For the training of multiple key-poses you can use the config/config_mapping.yaml
file and run scripts_pose_tracking/train_mapping.py -c config/config_mapping.yaml
.
Tracking the car pose in the trained submaps can be done using python3 scripts/pose_tracking.py experiments/PATH-TO-THE-CHECKPOINTS/best-v*.ckpt -vis
.
Pretrained models can be downloaded here and should be placed under /experiments
. Those models can be used as explained above.
Training on your own data
Most importantly implement your own dataloader. An example can be seen in src/loc_ndf/datasets/datasets.py
. Second, exchange the dataloader in the training script by you dataloader. Ready to train.
2D - MCL
The data can be downloaded here. The training data consists of poses.txt
and scans/*.npy
. The evaluation is done on seq1 to seq5 using the provided scans as well as the odometry.txt
. The extrected files are expected to be in in data/
.
Training
For training a model you can configure scripts_mcl/config.yaml
and run scripts_mcl/train.py
.
After training a model, one can run the MCL example, e.g. (scripts_mcl/run_mcl.py -c PATH-TO-YOUR_CKPT -i data/indoor_scan_poses_2d/seqX -cal data/indoor_scan_poses_2d/base2laser.txt -o out_poses.txt
) with the trained model.
The Pretrained models can be downloaded here and should be placed under /experiments
.
Citation
If you use this library for any academic work, please cite the original paper.
@article{wiesmann2023ral,
author = {L. Wiesmann and T. Guadagnino and I. Vizzo and N. Zimmerman and Y. Pan and H. Kuang and J. Behley and C. Stachniss},
title = {{LocNDF: Neural Distance Field Mapping for Robot Localization}},
journal = ral,
volume = {8},
number = {8},
pages = {4999--5006},
year = 2023,
issn = {2377-3766},
doi = {10.1109/LRA.2023.3291274},
codeurl = {https://github.com/PRBonn/LocNDF}
}