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https://csiro-robotics.github.io/Wild-Places/
🏞️ [IEEE ICRA2023] The official repository for paper "Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments" To appear in 2023 IEEE International Conference on Robotics and Automation (ICRA)
https://csiro-robotics.github.io/Wild-Places/
benchmark dataset deep-learning lidar loop-closure natural-environments place-recognition point-cloud
Last synced: 3 months ago
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🏞️ [IEEE ICRA2023] The official repository for paper "Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments" To appear in 2023 IEEE International Conference on Robotics and Automation (ICRA)
- Host: GitHub
- URL: https://csiro-robotics.github.io/Wild-Places/
- Owner: csiro-robotics
- Created: 2022-09-15T04:40:08.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-06-16T03:24:06.000Z (over 1 year ago)
- Last Synced: 2024-07-31T02:34:32.089Z (3 months ago)
- Topics: benchmark, dataset, deep-learning, lidar, loop-closure, natural-environments, place-recognition, point-cloud
- Language: Python
- Homepage: https://csiro-robotics.github.io/Wild-Places/
- Size: 92.3 MB
- Stars: 69
- Watchers: 13
- Forks: 3
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-3D-LiDAR-Datasets - Wild Places - 16C|No|PR|Large| (Summary Table / Update: 2023-07-12)
README
# Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments
## [Website](https://csiro-robotics.github.io/Wild-Places/) | [Paper](https://arxiv.org/abs/2211.12732) | [Data Download Portal](https://data.csiro.au/collection/csiro:56372?q=wild-places&_st=keyword&_str=1&_si=1)
This repository contains the code implementation used in the paper *Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments*, which has been accepted for publication at ICRA2023.
If you find this dataset helpful for your research, please cite our paper using the following reference:
```
@inproceedings{2023wildplaces,
title={Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments},
author={Knights, Joshua and Vidanapathirana, Kavisha and Ramezani, Milad and Sridharan, Sridha and Fookes, Clinton and Moghadam, Peyman},
year={2023},
booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
eprint={arXiv preprint arXiv:2211.12732}
}
```
## Contents
1. [Updates](#1-updates)
2. [Download Instructions](#2-download-instructions)
3. [Scripts](#3-scripts)
* [Environment](#31-environment)
* [Loading Point Clouds](#32-loading-point-clouds)
* [Generating Training & Testing Splits](#32-generating-training--testing-splits)
* [Training](#training)
* [Testing](#testing)
* [Evaluation](#33-evaluation)
* [Inter-Run](#inter-run-evaluation)
* [Intra-Run](#intra-run-evaluation)
4. [Thanks](#4-thanks)
## 1. Updates
- **21/10/2022** Wild-Places v1.0 Uploaded
- **17/01/2023** Wild-Places is accepted to ICRA2023!
- **20/01/2023** Wild-Places v2.0 Uploaded. This update to the dataset integrates GPS into the SLAM solution to alleviate vertical drift in the larger loops of the traversal in both environments. NOTE: Sequence K-04 is currently unavailable for v2.0 due to a failed loop closure in the ground truth. We are currently working on remedying this, and will release the sequence as soon this issue is rectified.
## 2. Download Instructions
Our dataset can be downloaded through [The CSIRO Data Access Portal](https://data.csiro.au/collection/csiro:56372?q=wild-places&_st=keyword&_str=1&_si=1). Detailed instructions for downloading the dataset can be found in the README file provided on the data access portal page.
## 3. Scripts
### 3.1 Environment
To create a python environment to use the scripts in this repository run the following command:
```
conda env create -f scripts/Wild-Places.yaml -n Wild-Places
```
### 3.2 Loading Point Clouds
A code snippet to load a pointcloud file from our dataset can be found in `eval/load_pointcloud.py`
### 3.2 Generating Training & Testing Splits
In this repository we provide several scripts for partitioning our dataset into splits for training and evaluation.
The output of these scripts are pickle files containing training and evaluation splits in a format compatible with existing repositories such as [PointNetVLAD](https://github.com/mikacuy/pointnetvlad), [MinkLoc3D](https://github.com/jac99/MinkLoc3D)([v2](https://github.com/jac99/MinkLoc3Dv2)), [TransLoc3D](https://github.com/slothfulxtx/TransLoc3D) and [PPT](https://github.com/fpthink/PPT-Net).
#### __Training__
To generate the training splits run the following command:
```
python scripts/generate_splits/training_sets.py --dataset_root $_PATH_TO_DATASET --save_folder --$_SAVE_FOLDER_PATH
```
Where `$_PATH_TO_DATASET` is the path to the downloaded dataset, and `$_SAVE_FOLDER_PATH` is the path to the directory where the generated files will be saved.
#### __Testing__
To generate the testing splits run the following command:
```
python scripts/generate_splits/testing_sets.py --dataset_root $_PATH_TO_DATASET --save_folder --$_SAVE_FOLDER_PATH
```
This script will generate seperate testing pickles for the inter-run and intra-run evaluation modes on each environment. The inter-run pickles will produce query and database files for each testing environment, while the intra-run pickles will produce a seperate training pickle for each individual point cloud sequence.
### 3.3 Evaluation
We provide evaluation scripts for both inter and intra-run evaluation on our dataset.
#### __Inter-run Evaluation__
To perform inter-run evaluation on the Wild-Places dataset, run the following command:
```
python eval/inter-sequence.py \
--queries $_PATH_TO_QUERIES_PICKLES \
--databases $_PATH_TO_DATABASES_PICKLES \
--query_features $_PATH_TO_QUERY_FEATURES \
--database_features $_PATH_TO_DATABASE_FEATURES \
--location_names $_LOCATION_NAMES \
```
Where:
- `$_PATH_TO_QUERIES_PICKLES` is a string pointing to the location of the generated query set pickle for an environment
- `$_PATH_TO_DATABASES_PICKLES` is a string pointing to the location of the generated database set pickle for an environment
- `$_PATH_TO_QUERY_FEATURES` is a string pointing towards a pickle file containing the query set features to be used in evaluation. These features should be a list of Nxd numpy arrays or tensors, where N is the number of point cloud frames in the query set of each sequence in the environment.
- `$_PATH_TO_DATABASE_FEATURES` is a string pointing towards a pickle file containing the database set features to be used in evaluation. These features should be a list of Nxd numpy arrays or tensors, where N is the number of point cloud frames in the database set of each sequence in the environment.
- `$_LOCATION_NAMES` is a string containing the name of the environment being evaluated
#### __Intra-run Evaluation__
To perform intra-run evaluation on the Wild-Places dataset, run the following command:
```
python eval/intra-sequence.py \
--databases $_PATH_TO_DATABASES_PICKLES \
--database_features $_PATH_TO_DATABASE_FEATURES \
--run_names $_LOCATION_NAMES \
```
Where:
- `$_PATH_TO_DATABASES_PICKLES` is a string pointing to the location of the generated database set pickle for a single sequence
- `$_PATH_TO_DATABASE_FEATURES` is a string pointing towards a pickle file containing the run features to be used in evaluation. These features should be a single Nxd numpy array or tensor, where N is the number of point cloud frames in that sequence
- `$_LOCATION_NAMES` is a string containing the name of the sequence being evaluated
## 4. Thanks
Special thanks to the authors of the [PointNetVLAD](https://github.com/mikacuy/pointnetvlad) and [MinkLoc3D](https://github.com/jac99/MinkLoc3D), whose excellent code was used as a basis for the generation and evaluation scripts used in this repository.