soooooda69 / oneslam_arthro

Tracking SLAM

This folder contains the code to run the SLAM algorithm and generate dense reconstructions.

Installing

To install the pipeline navigate to Tracking_SLAM/CoTrackSLAM and run

source install.sh

This will install a conda enviroment named TrackingSLAM as well as g2opy. Note that g2opy requires cmake to be installed. The script was tested on Ubuntu 20.04.6 LTS.

Running

To run on your data, setup a directory as follows

└── data
    ├── images
    │   ├── 00000000.jpg
    │   ├── 00000001.jpg
    │   ├── 00000002.jpg
    │   └── ...
    ├── calibration.json
    ├── [mask.bmp]
    └── [poses_gt.txt]

For the structure of the calibration.json file, please view the example provided in data_example. Note that while the intrinsics are required, the FPS is currently not used. The poses are required to be in TUM format with the frame index being used as the timestamp. The poses are assumed to be the Camera-To-World transformations.

After setting up the data directory, navigate to run_slam.py and set data_root to the path of your data directoy. Moreover, define the frames to be used by setting start_idx and end_idx to the first and last frame indices to be included.

Then, to start the slam pipeline run

python run_slam.py

After the run is finished, the output+visualizations will be saved in the folder experiments/TIMESTAMP. Note that currently creating the point trajectory visualization takes very long if tracking a lot points.