Comments (2)
Chen-Xieyuanli
commented on June 14, 2024
Hey @zhangjun-xyz,
thank you for following our work!
This is a very good question. We haven't tried other types of LiDAR sensors so far. However, since the network is quite light and simple, one could easily adapt OverlapNet to other types of input. The only part that needs to be adapted is the shape of the leg.
We currently have no plan to test other types of sensors and warmly welcome you and other users to fork our repo to give a try. If you have any further questions please feel free to drop me msgs or send me e-mails. My e-mail address is: [email protected]
Best,
from overlapnet.
zhangjun-xyz
commented on June 14, 2024
I understand. It's unrealistic for you guys to test all the sensors on the market. I am just curious about the performance on vlp-16 and see whether you've done it.
I am not sure whether I am able to test it myself. But if I do this I will let you know.
from overlapnet.
Related Issues (20)
- Questions about my test HOT 2
- Questions about overlapnet HOT 2
- Using overlapnet for a different dataset HOT 6
- Question about covariance ? HOT 2
- Question about computing overlap ground truth HOT 4
- Way to integrate with ROS ?? HOT 5
- Preprocessing time HOT 2
- Question about how to judge a loop closure True Positive or not HOT 4
- FileNotFoundError: [Errno 2] No such file or directory: 'config/demo.yml'
- some errors encountered during training HOT 7
- Some confusion about normalize_data.py HOT 3
- Requirements of pytorch HOT 3
- Some confusion about real overlap calculation HOT 10
- Pytorch version training. HOT 3
- Questions when Generating train_set HOT 5
- Pytorch version training HOT 6
- Problem in the first version HOT 2
- ground truth HOT 1
- Datasets HOT 1
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