Comments (1)
Thanks for your interest in our work.
Indeed, one solution would be using scripts/rosbag2ply.py and a shell script to convert all the rosbags.
However, as you can imagine, this is a bit cumbersome.
Now the workaround is to use the kiss-icp dataloader. The current version of PIN-SLAM supports to use the kiss-icp data loader. You may add (edit) the following contents to the config file:
setting:
.....
use_kiss_icp_dataloader: True
pc_path: "./data/vbr/vbr_slam/spagna/spagna_test0" # or other sequences
.....
For using the kiss-icp data loader, install kiss-icp by:
pip install kiss-icp
Then simply run:
python pin_slam.py ./config/lidar_slam/run_vbr.yaml rosbag /ouster/points
Currently, this rosbag dataloader does not support the loading of ground truth poses.
Therefore, you need to evaluate the results after the SLAM.
Here are some sample results that you can achieve with PIN-SLAM on the cool Rome dataset:
from pin_slam.
Related Issues (20)
- How many GPU memory need for generate a full scene mesh video like [demo_kitti00.mp4 ] ? HOT 1
- Attitude drift after making a turn in custom dataset HOT 12
- Unexpected artifacts when only perform mapping with gt pose. HOT 2
- Difficult to operate at the sensor frequency in kitti 00 HOT 2
- About time consumption HOT 2
- How to use Replica dataset HOT 2
- signal frame HOT 2
- odometry evaluation HOT 4
- CUDA error even when using cpu yaml HOT 1
- question about parameter correct_deg:0.195 in run_kitti.yaml HOT 2
- Would the training code be released? HOT 1
- issue about mesh HOT 5
- LiDAR points and Visual RGB HOT 2
- Have individual data loader for each dataset HOT 1
- Add pure-localization mode
- Have the values of `local_neural_points` been modified between being selected from and reassigned to global neural points? HOT 2
- torch.cuda.OutOfMemoryError: CUDA out of memory HOT 4
- How to understand the stability of neural points? HOT 4
- Clarify dependencies / requirements HOT 2
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from pin_slam.