https://github.com/dlr-rm/s3li-toolkit
https://github.com/dlr-rm/s3li-toolkit
Last synced: 7 months ago
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- Host: GitHub
- URL: https://github.com/dlr-rm/s3li-toolkit
- Owner: DLR-RM
- License: mit
- Created: 2025-03-13T10:41:26.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-03-27T09:26:50.000Z (10 months ago)
- Last Synced: 2025-06-06T16:09:44.175Z (7 months ago)
- Language: Python
- Size: 1.66 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# S3LI Interface
This package provides useful tools and scripts for playback and preparation of the S3LI dataset.
Links:
- https://datasets.arches-projekt.de/s3li_dataset/
- https://ieeexplore.ieee.org/document/9813579
If you find this package useful for your work, consider to cite
```
@ARTICLE{9813579,
author={Giubilato, Riccardo and Stürzl, Wolfgang and Wedler, Armin and Triebel, Rudolph},
journal={IEEE Robotics and Automation Letters},
title={Challenges of SLAM in Extremely Unstructured Environments: The DLR Planetary Stereo, Solid-State LiDAR, Inertial Dataset},
year={2022},
volume={7},
number={4},
pages={8721-8728},
doi={10.1109/LRA.2022.3188118}
}
```
## Generation of train and test datasets for place recognition
The package provides two scripts to scrim the bagfiles and generate datasets with synchronized tuples of:
$$(I_{left}, L, p_{D-GNSS}, \phi_{north})$$
with $I_{left}$ the left camera image, $L$ a lidar scan, $p_{D-GNSS}$ the ground truth position in global coordinates, measured with a differential GNSS setup, and $\phi_{north}$ the orientation of the camera to the north.
### 1. Preparation
Download the dataset :) (https://datasets.arches-projekt.de/s3li_dataset/)
### 2. Set-Up
This code was tested using Python 3.9.21 and ROS1 Melodic on openSUSE Leap 15.5. To start, clone the repository and then use the provided requirements.txt to install the dependencies:
`pip install -r requirements.txt`
### 3. Pre-processing step: SLAM evaluation data to pickle
As the D-GNSS setup did not measure orientation of the camera, we approximate it using the results from the evaluated visual-inertial SLAM/Odometry algorithms. The idea is that, after aligning trajectories and camera poses to the D-GNSS ground truth, transformed into metric coordinates for an arbitrary ENU frame, the pose estimation should be accurate enough to provide a very good guess of the real orientatio of the cameras. Therefore, we can use this information to better discern between positive and negative samples for place recognition.
The script `slam_poses_to_pandas.py` reads the output `.txt` files from the `Eval` folder, and transforms them into a pandas dataframe. The dataframe will contain SLAM/VO results in the following tabular form:
| Timestamp | Rotation | Position |
| -------- | ------- |------- |
| posix_time [s] | Quaternion **(w, x, y, z)** | [x, y, z] |
Usage:
1) From the root package folder: ```python3 scripts/slam_poses_to_pandas.py ${path_to_s3li_dataset_root} ${which_algorithm}``` (e.g., ```python3 scripts/slam_poses_to_pandas.py /home_local/giub_ri/s3li_dataset BASALT_STEREO_INERTIAL```)
2) This will write pickle files in a folder names `processed` from the root dataset path
### 4. Sample Generation
This step foresees the generation of all data samples for each sequence. The script `create_dataset.py` reads the content of all bagfiles in the `Bagfile` folder, and associates data from the various topics, writing links and data into a pandas dataframe, then stored as a pickle to disk.
In this step, the results of SLAM evaluations, pre-processed in Sec.2, are used to approximate an orientation (with respect to the North) for each image/scan pair, as the ground truth is only from D-GNSS without magnetometer or full RTK. To account for the yaw-drift, which is inevitable in the context of V-SLAM, camera trajectories are split in an user-defined manner (default is 3 splits) and independently aligned to the ground truth (x, y, z in ENU frame). The corresponding rotation is applied to the camera poses, and the northing is stored.
Usage:
1) From the package root: ```python3 scripts/create_dataset.py ${path_to_s3li_dataset_root} ${camera_config}``` (e.g. ```python3 scripts/create_dataset.py /home_local/giub_ri/s3li_dataset/ cfg/cam0_pinhole.yaml```)
### 5. Lidar Histograms Generation
This script `create_lidar_histograms.py` processes LiDAR point cloud data stored in the pickle files and generates histograms representing the distribution of depth (Z-axis) values. The generated histograms will later be used in the visualization of the interactive plot.
Usage:
1) From the package root:```python3 scripts/create_lidar_histograms.py ${path_to_s3li_dataset_root}```
`
### 6. Visualization
The results can be qualitatively inspected using the provided script `make_maps_in_bokeh.py`, which overlays subsampled images (with LiDAR point projections) on geo-references positions in an interactive map.
Usage:
1) From the package root: ```python3 scripts/make_maps_in_bokeh.py ${path_to_s3li_dataset_root}/dataset/ ${skip}```
This will look in the dataset folders for saved pickles, that contain a dataframe for each entire bagfile, and create an interactive Bokeh plot showing the global position and orientation of collected image/scan samples and pre-vieweing the image with lidar overlay when hovering with the mouse cursor.
Example Bokeh plot, full view:
Detail of the Bokeh plot, with data preview:
### 7. Interactive plot
Visual analysis interactive tool for camera overlap.
The script `interactive_overlap_maps.py` calculates the overlap between different camera views using two possible methods:
- An angle-based approach (compute_overlap_v1)
- A polygon intersection approach (compute_overlap_v2)
In addition, it considers occlusion detection by analyzing LiDAR depth data to adjust camera field-of-view ranges.
Usage:
1) From the package root: ```python3 scripts/interactive_overlap_maps.py ${path_to_s3li_dataset_root}/dataset/ ${skip} ${compute_overlap_version}``` (e.g. ```python3 scripts/interactive_overlap_maps.py /home_local/enci_la/Etna/dataset/ 50 2```)
Example Bokeh plot:
