jedeschaud / ct_icp

CT-ICP: Continuous-Time LiDAR Odometry
MIT License
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why my Elapsed Search Neighbors cost too much? can you answer for me,thanks #3

Closed dongfangzhou1108 closed 2 years ago

dongfangzhou1108 commented 2 years ago

thanks for openning your code.

Number of points in sub-sampled frame: 35679 / 126902 Initial ego-motion distance: 0 Elapsed Normals: 35.0737 Elapsed Search Neighbors: 567.692 Elapsed A Construction: 0 Elapsed Select closest: 2.18221 Elapsed Solve: 0.222377 Elapsed Solve: 8.04642 Number iterations CT-ICP : 5 Elapsed Elastic_ICP: 681.094 Number of Keypoints extracted: 4640 / Actual number of residuals: 4180 Trajectory correction [begin(t) - end(t-1)]: 0 Final ego-motion distance: 0.29746 Average Load Factor (Map): 0.324829 Number of Buckets (Map): 16384 Number of points (Map): 39514 Elapsed Time: 683.08 (ms)

here is my log info of one frame, why my Elapsed Search Neighbor cost too much?

best wishes

dongfangzhou1108 commented 2 years ago

and i play by default config file

dongfangzhou1108 commented 2 years ago

i guess first 20 frames cost much?

dongfangzhou1108 commented 2 years ago

here is my kitti raw 09 output image can see it lost a lot...

dongfangzhou1108 commented 2 years ago

i modify your code on ros,have a bad output, but when on your own code , it is no problem,

pdell-kitware commented 2 years ago

Hi, thank you for your interest

For your first question, for the first 20 frames (the time to construct the map), we use a larger neighborhood (inspecting the 5^3=125 closest voxels instead of the 3^3=27 neighbors). This requires more time for the first 20 frames, but insure a good initialization.

I do not really understand your second question, but we will work on our own ROS support in a month or so. Your results on kitti_raw sequence 09 seem very inconsistent with what we observe on the dataset running our code. You can use our dataset of PLY files (link in the Readme), and you should have much cleaner results.

Best regards,

dongfangzhou1108 commented 2 years ago

Hi, thank you for your interest

For your first question, for the first 20 frames (the time to construct the map), we use a larger neighborhood (inspecting the 5^3=125 closest voxels instead of the 3^3=27 neighbors). This requires more time for the first 20 frames, but insure a good initialization.

I do not really understand your second question, but we will work on our own ROS support in a month or so. Your results on kitti_raw sequence 09 seem very inconsistent with what we observe on the dataset running our code. You can use our dataset of PLY files (link in the Readme), and you should have much cleaner results.

Best regards,

sorry, i do not make time from 0 to 1, when i change this, everything goes right. thanks for your reply.

best wishes