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https://github.com/YukunXia/VLOAM-CMU-16833
CMU 16-833 "Robot Localization and Mapping" Course Project
https://github.com/YukunXia/VLOAM-CMU-16833
loam localization mapping slam
Last synced: 3 months ago
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CMU 16-833 "Robot Localization and Mapping" Course Project
- Host: GitHub
- URL: https://github.com/YukunXia/VLOAM-CMU-16833
- Owner: YukunXia
- License: mit
- Created: 2021-03-23T05:23:06.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2021-07-13T02:04:25.000Z (almost 3 years ago)
- Last Synced: 2024-01-29T11:14:33.951Z (5 months ago)
- Topics: loam, localization, mapping, slam
- Language: C++
- Homepage:
- Size: 62.6 MB
- Stars: 153
- Watchers: 7
- Forks: 40
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-stars - YukunXia/VLOAM-CMU-16833 - CMU 16-833 "Robot Localization and Mapping" Course Project (C++)
README
# Introduction
This repository is a reimplementation of the VLOAM algorithm [1]. The LOAM/Lidar Odometry part is adapted and refactored from ALOAM [2], and the Visual Odometry part is written according to the DEMO paper [3].
The following figure [1] illustrates the pipeline of the VLOAM algorithm.
![demo](figures/VLOAM-figure1.png)
# Results
![demo](figures/results.png)
Video: https://youtu.be/NnoxB3r_cDM
![demo](figures/evaluation.png)
# Detailed Usage
Check README.md under `src/vloam_main`
## PrerequisitesOpenCV 4.5.1
Eigen3 3.3
Ceres 2.0
PCL 1.2## Evaluation tool
![demo](figures/kitti_car.png)
https://github.com/LeoQLi/KITTI_odometry_evaluation_tool
## Data format
Place bag files under "src/vloam_main/bags/"
Note: current dataloader only support "synced" type dataset.
# Reference:
[1] J. Zhang and S. Singh. Laser-visual-inertial Odometry and
Mapping with High Robustness and Low Drift. Journal of
Field Robotics. vol. 35, no. 8, pp. 1242–1264, 2018.[2] T. Qin and S. Cao. A-LOAM. https://github.com/HKUST-Aerial-Robotics/A-LOAM
[3] Zhang, Ji, Michael Kaess, and Sanjiv Singh. "Real-time depth enhanced monocular odometry." 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2014.