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Repository Summary
| Checkout URI | https://github.com/tier4/lidar_feature_extraction.git |
| VCS Type | git |
| VCS Version | develop |
| Last Updated | 2022-12-26 |
| Dev Status | UNMAINTAINED |
| CI status | No Continuous Integration |
| Released | UNRELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Packages
| Name | Version |
|---|---|
| ekf_localizer | 0.1.0 |
| lidar_feature_extraction | 0.1.0 |
| imu_integration | 0.0.0 |
| kalman_filter | 0.1.0 |
| lidar_feature_library | 0.0.0 |
| lidar_feature_launch | 0.0.0 |
| lidar_feature_localization | 0.1.0 |
| lidar_feature_map_loader | 0.0.0 |
| map_tf_generator | 0.1.0 |
| lidar_feature_mapping | 0.1.0 |
| path_generator | 0.0.1 |
| point_type_converter | 0.0.0 |
| rotationlib | 0.0.0 |
README
LiDAR feature-based localization
Functionalities
This package has two functionalities:
- Map building module: constructs a map from undistorted LiDAR scans and their poses
- Localization module: localizes LiDAR on a map
Benefits
- LOAM-like feature based algorithm enables localization in challenging environments such as tunnels, rice fields, etc.
- Localization on a pre-built map realizes stable and robust localization in dynamic environments
Citation
This code makes use of LIO-SAM and LeGO-LOAM.
@inproceedings{liosam2020shan,
title={LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping},
author={Shan, Tixiao and Englot, Brendan and Meyers, Drew and Wang, Wei and Ratti, Carlo and Rus Daniela},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={5135-5142},
year={2020},
organization={IEEE}
}
@inproceedings{legoloam2018shan,
title={LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain},
author={Shan, Tixiao and Englot, Brendan},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={4758-4765},
year={2018},
organization={IEEE}
}
CONTRIBUTING
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