Add PointCloudSamplerWithNormal #339
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[#847] FAILED on kinetic
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[#848] FAILED on melodic
[#848] PASSED on noeticTested on Alpine ROS [#848] PASSED on kineticAll tests passed |
[#849] FAILED on melodic
[#849] PASSED on noeticTested on Alpine ROS [#849] PASSED on kineticAll tests passed |
[#850] FAILED on noeticTested on Alpine ROS [#850] PASSED on melodicAll tests passed[#850] PASSED on kineticAll tests passed[#850] PASSED on noeticTested on Alpine ROS |
Codecov Report
@@ Coverage Diff @@
## master #339 +/- ##
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- Coverage 94.56% 94.01% -0.55%
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Files 32 34 +2
Lines 1729 1806 +77
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+ Hits 1635 1698 +63
- Misses 94 108 +14
Continue to review full report at Codecov.
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@at-wat PTAL. |
[#851] PASSED on noeticTested on Alpine ROS [#851] PASSED on melodicAll tests passed[#851] PASSED on kineticAll tests passed |
at-wat
reviewed
Sep 18, 2020
| using SamplerType = PointCloudRandomSampler<PointType>; | ||
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| LidarMeasurementModelBase() | ||
| : sampler_() |
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I think it's better to be set by a constructor argument (from derived class constructor) as it will cause null pointer error if setRandomSampler is not called.
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Or, it can be passed as an argument of LidarMeasurementModel::filter().
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PointCloudUniformSampler is set as a default.
| return output; | ||
| } | ||
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| const double eigen_value_ratio = std::sqrt(eigen_values_[2]) / std::sqrt(eigen_values_[1]); |
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Suggested change
| const double eigen_value_ratio = std::sqrt(eigen_values_[2]) / std::sqrt(eigen_values_[1]); | |
| const double eigen_value_ratio = std::sqrt(eigen_values_[2] / eigen_values_[1]); |
| { | ||
| acos_angle = 1.0; | ||
| } | ||
| const double angle = std::acos(acos_angle); |
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I'd like to avoid trigonometric functions in the loop of the input points as much as possible.
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I agree, but it takes less than 0.2ms to process this block on my laptop, and the optimization benefits will be small if it is removed. (It takes around 10ms to estimate normals.)
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It looks fine as it is not a default option.
Maybe it's better to add short description to algorithm and parameter documents later.
[#852] PASSED on noeticTested on Alpine ROS [#852] PASSED on melodicAll tests passed[#852] PASSED on kineticAll tests passed |
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@at-wat Please take another look. |
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When the first principal component of particle positions are much larger than the second, PointCloudSamplerWithNormal selectively samples scan points that have small angular difference between their normals and the first principal component.
After a robot moves along a long corridor without features to estimate travel distance, particles can be converged quickly with this algorithm soon after the robot finds a side way.