feat(blockage_diag): dust detection

[swiftfile]

Description

I implemented dust detection process.
When dust covers the lidar, noise is added to the depth image. Therefore, I thought that noise(dust area) can be detected by applying noise reduction algorithms.
So,I implemented an algorithm used for noise reduction, morphological processing.

Here are the results of my implementation.
This is a video of rosbag with dust(only front_upper) on the lidar.
The second image from the top is a depth image, showing noise in the area where the dust is attached.
The top image is the output image when the process is executed.
The red color indicates the result of detecting blockage area, which has been implemented previously.
The yellow color indicates the result of detecting dusty area, which is result of my new implementation.

Related links

tier4 INTERNAL LINK : about this dust detection

Related PR

feat: add params and diag about dust-detection by swiftfile · Pull Request #116 · tier4/aip_launcher
feat(blockage_diag): dust detection by swiftfile · Pull Request #3192 · autowarefoundation/autoware.universe (cancelled PR)

Tests performed

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[VRichardJP]

Sorry i could not understand. Where is the dust located on the lidar in your demo?

Would your algorithm also work with other things, such as water drops, snow, bird 🐦💩?

[badai-nguyen]

@VRichardJP
Thanks for your comment.

1. This implementation was tested on the data collected from Hesai QT LiDAR which was sprayed by Arizona test dust
   Here is how the LiDAR looks like after sprayed dust few times compressor
   

2. IMO, the algorithms target to detect some sparse and tiny dust which adhered and blocked the LiDAR rays so maybe not work well with kind of clean water/rain drop. Related to snow case, since the data not available yet so I have no comment for now.
   cc @drwnz Do you have other comments?

[stale]

This pull request has been automatically marked as stale because it has not had recent activity.

[codecov]

Codecov Report

Attention: 178 lines in your changes are missing coverage. Please review.

Comparison is base (bf03fe3) 15.02% compared to head (0b5b4b7) 14.96%.
Report is 68 commits behind head on main.

Additional details and impacted files

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Flag	Coverage Δ		*Carryforward flag
differential	5.48% <0.00%> (?)
total	15.04% <ø> (+0.01%)	⬆️	Carriedforward from 93cee3c

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Files	Coverage Δ
...cessor/src/blockage_diag/blockage_diag_nodelet.cpp	0.00% <0.00%> (ø)

... and 9 files with indirect coverage changes

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[badai-nguyen]

I have reproduced the result using logging_simulator as below:

LGTM
@miursh could you approve this PR?

[miursh]

LGTM. Thank you for your support.