Comments (2)
Hi @james-yoo ,
The GNM training data certainly has a diverse variety of cameras used for data collection (and across different datasets, we did not enforce common cameras), but the data sources unanimously used cameras with a large FoV. The main motivation behind using a wide-angle camera is because the large FoV helps localization and improve distance estimates. This is generally true for all situations, and a larger FoV almost certainly improves collision avoidance as well as mitigate partial observability concerns (as opposed to something with a narrow FoV like a RealSense).
We did all our experiments with commercial fisheye cameras with two properties -- large FoV and low latency. You can also try narrower FoV and it might work, but possibly a bit worse. If you want to use a spherical camera, this might be a bit difficult since they are a lot more OOD with respect to the training data.
If you have a strong case to use other types of camera (e.g., RealSense or a spherical camera), please check out our concurrent research on using geometric priors to enable generalization to diverse camera parameters.
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@PrieureDeSion,
Great. I will check ExAug. Thank you!
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Related Issues (19)
- Code and data release HOT 2
- Dataset Sampling HOT 2
- some question about create_topomap.py HOT 2
- Missing the odometry topic in the recording script HOT 2
- some question about depolyment with CARLA HOT 3
- ./create_topomap.sh Image with strange color HOT 2
- Minor bug in deployment side image_transform HOT 1
- Some questions about running the project in the CARLA simulator HOT 2
- Question on data sampling rate HOT 1
- Code and data release HOT 2
- Two forward passes during training HOT 3
- Is there documentation for a successful deployment in carla? HOT 2
- Training Dataset
- GNM models cannot avoid obstacles
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- Deployed in the simulation environment HOT 2
- rosbag file for deployment HOT 4
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