X-lanni
50 lines
2.3 KiB
Markdown
50 lines
2.3 KiB
Markdown
Docker image used to reproduce all results for KITTI, EuRoC and TUM datasets of the following paper (for MIT Stata Center dataset, see this [page](https://github.com/introlab/rtabmap_ros/blob/master/launch/jfr2018)):
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* M. Labbé and F. Michaud, “RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation,” in Journal of Field Robotics, accepted, 2018. ([pdf](https://introlab.3it.usherbrooke.ca/mediawiki-introlab/images/7/7a/Labbe18JFR_preprint.pdf)) ([Wiley](https://doi.org/10.1002/rob.21831))
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Pull image:
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```bash
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$ docker pull introlab3it/rtabmap:jfr2018
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```
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or create the image with the `Dockerfile` provided in this directory:
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```bash
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$ cd rtabmap/docker/jfr2018
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$ docker build -t introlab3it/rtabmap:jfr2018 .
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```
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Make sure to extract datasets in a subfolder called `datasets` relative to scripts in this folder (you can copy the scripts outside rtabmap source directory for convenience). The testing folder tree should look like this:
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```bash
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datasets/kitti/devkit
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datasets/kitti/dataset/sequences/00
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datasets/kitti/dataset/sequences/01
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datasets/kitti/dataset/sequences/...
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datasets/euroc/MH_01_easy
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datasets/euroc/V1_01_easy
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datasets/euroc/...
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datasets/tum/rgbd_dataset_freiburg1_desk
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datasets/tum/rgbd_dataset_freiburg3_long_office_household
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datasets/tum/...
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run_all.sh
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run_kitti_datasets.sh
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run_euroc_datasets.sh
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run_tum_datasets.sh
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```
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For TUM dataset, use this script [associate.py](https://gist.github.com/matlabbe/484134a2d9da8ad425362c6669824798) to synchronize RGB and depth images before processing. Usage in a TUM dataset: `python associate.py rgb.txt depth.txt`, this will create `rgb_sync` and `depth_sync` folders.
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Process all datasets, results will be written to subfolders `results/euroc`, `results/kitti` and `results/tum`:
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```bash
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./run_all.sh
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```
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WARNING: processing all datasets with all different odometry approaches can require more than 10 hours to process. You can comment some configurations in [run_all.sh](https://github.com/introlab/rtabmap/blob/master/docker/jfr2018/run_all.sh) if you are interested in just one odometry approach or one dataset. A single sequence can be tested like this too:
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```bash
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# Processing only sequence 07 of the kitti dataset with F2M odometry approach
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./run_kitti_datasets.sh f2m 0 0 07
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```
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Show all results:
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```bash
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$ rtabmap-report --scale results
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```
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