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princeton-vl / DPVO

Deep Patch Visual Odometry/SLAM
MIT License
609 stars 71 forks source link

bad result on kitti #4

Open whitesockcat opened 2 years ago

whitesockcat commented 2 years ago

image image

is there any way to solve this scale problem?

zachteed commented 2 years ago

Yeah that doesn’t look good, thanks for bringing this up. It looks like there is some bias in the driving scenarios causing the scale to constantly expand.

I will look into this and reply to this thread in a few days. There might be a simple fix, maybe not. The hacky solution would be to impose a prior on depth of the road immediately in front of the car but that’s not ideal

whitesockcat commented 2 years ago

Thx😀, looking forward to getting your update soon

aniket-gupta1 commented 1 year ago

@whitesockcat what stride did you use for running on kitti?

whitesockcat commented 1 year ago

@whitesockcat what stride did you use for running on kitti? 1

RaymonHE commented 1 year ago

image image

is there any way to solve this scale problem?

Hi @whitesockcat , thanks for sharing the result on KITTI. I am new to KITTI and neural networks. I wonder how you test this algorithm on KITTI datasets. Do you just use KITTI as the input and implement the demo.py as shown in the README file, the same way as TartanAir, like python demo.py --imagedir=<path to image_left> --calib=calib/tartan.txt --stride=1 --viz, what's the format and how you set the calib and stride file? I would be very grateful if you can give me some suggestions.

GopiRajuMatta commented 1 year ago

image image

is there any way to solve this scale problem?

@zachteed and @whitesockcat,

When I am working with kitti odometry dataset(sequence-05), I also got the similar results, issue with scale. Any fix around? If so, please suggest. Waiting for your reply..

Thank you Gopi

nnop commented 1 year ago

Any fix on this issue?