The implementation of the paper 'DELO : Deep Evidential LiDAR Odometry using Partial Optimal Transport' (ICCV2023). ArXiv, Project Page
- Python 3.8
- CUDA 11.3 or above
- torch 1.12.1 and torchvision 0.14.1
- Open3d 0.15.2
- torchvision 0.13.1, torchmetrics 1.1.2
- Pytorch-Lightning 2.0.3
- lightning 2.0.9.post0
Create the conda environment using the following command (Only supports Linux OS). In future, we will setup a docker container to overcome any tedious environment setup that may arise.
conda env create -f environment.yml
conda activate DELO
-
Download the KITTI LiDAR odometry dataset from the
odometry benchmark. At the moment, one only needs to download (1) the Velodyne LiDAR data, (2) the groundtruth odometry poses, (3) the sensor calibration files and finally (4) the odometry development kit to debug if the downloaded files are okay. -
To replicate the exact evaluation and training samples as followed in DELO, run the training-split, testing-split, and evaluation-split by downloading the sample-Ids (e.g., 0000001, 00000008, ...) per odometry sequenes (e.g., 00, 02, 03, ...) saved as train.npy, test.npy, and eval.npy files from
./assets/train_test_eval_splits/. Place the files under the downloaded dataset folders such that they match the following data structure -
The expected data structure:
. └── KITTI ROOT PATH/ ├── results/ ├── datasets/ │ ├── poses/ ├ ├ ├── 00.txt ├ ├ ├── 01.txt ├ ├ ├── ... ├ ├ ├── 10.txt │ ├── sequences/ ├ ├ ├── 00/ ├ ├ ├ ├── poses.txt ├ ├ ├ ├── calib.txt ├ ├ ├ ├── times.txt ├ ├ ├ ├── train.npy ├ ├ ├ ├── eval.npy ├ ├ ├ ├── test.npy ├ ├ ├ ├── velodyne/ ├ ├ ├ ├ ├── {8_digit_sampleID1}.bin ├ ├ ├ ├ ├── {8_digit_sampleID2}.bin ├ ├ ├ ├ ├── {...}.bin ├ ├ ├ ├ ├── {8_digit_sampleIDN}.bin ├ ├ ├── 01/ ├ ├ ├ ├── poses.txt ├ ├ ├ ├── calib.txt ├ ├ ├ ├── times.txt ├ ├ ├ ├── train.npy ├ ├ ├ ├── eval.npy ├ ├ ├ ├── test.npy ├ ├ ├ ├── velodyne/ ├ ├ ├ ├ ├── {8_digit_sampleID1}.bin ├ ├ ├ ├ ├── {8_digit_sampleID2}.bin ├ ├ ├ ├ ├── {...}.bin ├ ├ ├ ├ ├── {8_digit_sampleIDN}.bin ├ ├ ├ ... ├ ├ ├ ... ├ ├ ├── 10/ ├ ├ ├ ├── poses.txt ├ ├ ├ ├── calib.txt ├ ├ ├ ├── times.txt ├ ├ ├ ├── train.npy ├ ├ ├ ├── eval.npy ├ ├ ├ ├── test.npy ├ ├ ├ ├── velodyne/ ├ ├ ├ ├ ├── {8_digit_sampleID1}.bin ├ ├ ├ ├ ├── {8_digit_sampleID2}.bin ├ ├ ├ ├ ├── {...}.bin ├ ├ ├ ├ ├── {8_digit_sampleIDN}.bin └──
The training scripts are provided to run on SLURM cluster.The script will automatically create logging folder (either train or test inside the /results/ folder or to the folder path set to the $LOGDIR variable inside slum_*.sh script files) and save the best checkpoints, as well as tensorboard log and any other metrics used for callback function. Therefore, appropritely set the environment variables
src/scripts/env.sh
To start training use train.py, e.g.
python3 train.py
--acceleratorType cuda \
--model_name {dcp|delo} \
--dataset_type Kitti \
--noise_type downsample \
--train_data_dir "$KITTI_TRAIN_DATASET_PATH" \
--test_data_dir "$KITTI_TEST_DATASET_PATH" \
--log_dir $LOGDIR \
--periodic_save_dir $LOGDIR \
--max_epochs 200 \
--num_points 1024 \
--train_batch_size 16 \
--val_batch_size 16 \
--head partial \
--use_evidence False
To train for DELO with Predictive Uncertainty for Evidential Pose Estimation, turn on the flag --use_evidence True
@inproceedings{ali2023delo,
title={DELO: Deep Evidential LiDAR Odometry using Partial Optimal Transport},
author={Ali, Sk Aziz and Aouada, Djamila and Reis, Gerd and Stricker, Didier},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={4517--4526},
year={2023}
}
@inproceedings{ali2021RPSRNet,
author={Ali, Sk Aziz and Kahraman, Kerem and Reis, Gerd and Stricker, Didier},
booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
title={RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut 2D-Tree Representation},
year={2021},
doi={10.1109/CVPR46437.2021.01290}
}