FinderNet: A Data Augmentation Free Canonicalization aided Loop Detection and Closure technique for Point clouds in 6-DOF separation.

We focus on the problem of LiDAR point cloud based loop detection (or Finding) and closure (LDC) in a multi-agent setting. State-of-the-art (SOTA) techniques directly generate learned embeddings of a given point cloud, require large data transfers, and are not robust to wide variations in 6 Degrees-of-Freedom (DOF) viewpoint. Moreover, absence of strong priors in an unstructured point cloud leads to highly inaccurate LDC. In this original approach, we propose independent roll and pitch canonicalization of the point clouds using a common dominant ground plane. Discretization of the canonicalized point cloud along the axis perpendicular to the ground plane leads to an image similar to Digital Elevation Maps (DEMs), which exposes strong spatial priors in the scene. Our experiments show that LDC based on learnt embeddings of such DEMs is not only data efficient but also significantly more robust, and generalizable than the current SOTA. We report significant performance gain in terms of Average Precision for loop detection and absolute translation/rotation error for relative pose estimation (or loop closure) on Kitti, GPR and Oxford Robot Car over multiple SOTA LDC methods. Our encoder technique allows to compress the original point cloud by over 830 times. To further test the robustness of our technique we create and opensource a custom dataset called Lidar-UrbanFly Dataset (LUF) which consists of point clouds obtained from a LiDAR mounted on a quadrotor.

The work is currently under review at IROS 2023, and repository completion in progress.

PrePrint: https://arxiv.org/pdf/2304.01074.pdf Video: https://www.youtube.com/watch?v=1P6JMqbb_sM

Installation Instructions

1. Install PyTorch
2. Install the requirements pip install -r requirements.txt

Canonicalization and DEM Creation

Download the Pre-Processed DEM

Please download the preprocessed DEM for various datasets in this link

DEM Generation for Custom Dataset

python3 DEM\ Generation/CreateDEM.py --PCD_path <Set the path to the directory of point clouds> --DEM_save_path <Path to save the resulting DEM>

Training FinderNet

python3 Train/Train.py --data_path <patht to the triplet csv file> --base_path <path to the DEM folder> --save_path <path to save the model> --continue_train <If true will start from the previous checkpoint> --path_to_prev_ckpt <set path if previous ckpt if continue_train is true>   

Inference

python3 Inference/Inference.py --base_path <Path to DEM folder> --ckpt_path <Path to the model> --pose_base_dir <Base path to the folder that consists of poses > 

Please download the pretrained models from the link

Paper

FinderNet: A Data Augmentation Free Canonicalization aided Loop Detection and Closure technique for Point clouds in 6-DOF separation.

If you use FinderNet please cite our paper (the preprint for now)

@article{harithas2023findernet,
  title={FinderNet: A Data Augmentation Free Canonicalization aided Loop Detection and Closure technique for Point clouds in 6-DOF separation},
  author={Harithas, Sudarshan S and Singh, Gurkirat and Chavan, Aneesh and Sharma, Sarthak and Patni, Suraj and Arora, Chetan and Krishna, K Madhava},
  journal={arXiv preprint arXiv:2304.01074},
  year={2023}
}

We welcome any suggestions to improve the readability of the code.