Survey of Point Cloud Registration Methods and New Statistical Approach
Abstract
:1. Introduction
1.1. Optical Rangefinder
1.2. 3D Point Cloud
2. A Survey of Methods
2.1. ICP Algorithm
2.1.1. ICP Algorithm Results
2.1.2. ICP Algorithm Problem
2.2. Normal Distribution Transform (NDT)
2.3. Feature Based Registration
2.4. IDC
2.5. pIC
2.6. Point-Based Probabilistic Registration
2.7. Gaussian Fields
2.8. Quadratic Patches
2.9. Likelihood-Field Matching
2.10. CRF Matching
2.11. Branch-and-Bound Registration
2.12. Registration Using Local Geometric Features
2.13. PoitnReg
2.14. The Automatic Mapping of Parametric Objects (Buildings) from Indoor Point Clouds
2.15. Basic Registration Methods Comparison
3. The Optimal Statistical Transformation Model Taking into Account Errors in Coordinate Systems
3.1. Model with Four Scans
3.2. Linearization of a Model with 4 Scans
3.3. The Estimates of Unknown Parameters in a Model with 4 Scans
3.4. Numerical Study
4. Concluding Remarks
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
. |
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Algorithm | Based on (Points/Model) | Covarience Matrix | Important Requirements | Speed Processing | Computation Complexity |
---|---|---|---|---|---|
3D ICP | points | is estimated | - | slow for large datasets | |
3D NDT | 3D normal distribution | is estimated | - | faster than ICP | |
Feature based | search space features | depends on application | varied environment | fast—depends on selected feature | depends on selected descriptor |
IDC | points | is estimated | - | faster than ICP | similar to ICP but not specified |
pIC | points | is estimated | - | slow for large datasets | similar to ICP but not specified |
Point-based probabilistic | Probability functions | ss estimated | - | faster than ICP | similar to ICP but not specified |
Gaussian fields | Gaussian mixture | is estimated | - | faster than ICP | |
Quadratic patches | Quadratic approximation | is estimated | - | faster than ICP | not specified |
Likelihood-fields | Likelihood-fields | uses identity matrix | - | similar to NDT | not specified |
CRF matching | Conditional random fields | uses log-likelihood function | make sense only in 2D | slow for large datasets | in current scan, in reference scan |
Branch and bound | Rotation symmetric features | is necessary, get from sensors | make sense only in 2D | not specified | not specified |
Local geometric features | Local geometric features | Is estimated | - | Depending on used algorithm | Depending on used algorithm |
PointReg | points | is necessary | only consistent object, moving has to be eliminated | slow due to user interaction | not specified |
Algorithm | Precision | Advantages | Necessity of Human Intervention | Suitable Application | Origin |
---|---|---|---|---|---|
3D ICP | good - depens on input data | precise with suitable data | initial parameters setup | precise 3D point clouds | 1992 |
3D NDT | good with suitable settings | space modeling, using of less accurate data | initial parameters setup | various input data | 2007 |
Feature based | good, depens on environment | working with camera images | initial parameters setup | robotic navigation | 1998, 2006 (real time) |
IDC | more robust, less precision than ICP | robustnes for bigger initial pose | initial parameters setup | similar to ICP | 1994 |
pIC | better accuracy, robustness and convergence than ICP and IDC | incorporates sensors quality and noise | initial parameters setup | similar to ICP | 2005 |
Point-based probabil | better than ICP | scans treating as probability function | initial parameters setup | large 3D scans | 2002 |
Gaussian fields | good with suitable settings | similar to 3D NDT | initial parameters setup | various input data | 2004 |
Quadratic patches | good with suitable settings | similar to 3D NDT | initial parameters setup | various input data | 2004 |
Likelihood-fields | similar to NDT | possiblity to extend scans to get more points | initial parameters setup | various input data | 2008 |
CRF matching | good with suitable settings | using od different user-features, robust to initial pose error | initial parameters setup | only for 2D scans | 2001 |
Branch and bound | not specifed, dependent on set of common symetric features | using on highly unstructured environments | initial parameters setup | natural outdoor environment | 2004 |
Local geometric feat | good depending on input data | matching local surfaces | initial parameters setup | point clouds wit varied objects | 1997 |
PointReg | low RMS with the suitable settings and data handling | good with good data handling | manual | pitfalls 3D range scans | 2011 |
Y | Y | Y | Y | Y | Y | Y | Y |
---|---|---|---|---|---|---|---|
629.1793 | 629.0871 | 521.6030 | 533.7651 | −457.5236 | −446.6342 | 208.9794 | 223.0744 |
84.1583 | 84.0498 | −464.5526 | −469.4856 | −258.9134 | −265.6701 | 702.7168 | 691.0423 |
15.1976 | 4.0980 | 4.0980 | 16.6208 | 26.6208 | 17.8997 | 22.8997 | 30.1976 |
626.6654 | 638.7188 | 532.2563 | 536.3855 | −451.5327 | −445.1702 | 209.2158 | 218.5299 |
87.9686 | 90.5740 | −468.8736 | −462.9402 | −262.6306 | −263.4124 | 700.0364 | 690.6115 |
3.5058 | 7.8426 | 7.8426 | 7.7437 | 17.7437 | 12.0001 | 17.0001 | 18.5058 |
622.4489 | 640.0699 | 533.7651 | 536.7300 | −450.7452 | −444.5112 | 209.3221 | 210.9074 |
94.3597 | 91.4826 | −469.4856 | −462.0798 | −263.1193 | −262.3962 | 698.8299 | 689.8890 |
4.0666 | 5.5998 | 5.5998 | 3.3267 | 13.3267 | 14.6452 | 19.6452 | 19.0666 |
622.0117 | 536.5612 | −451.1310 | −443.3154 | 209.5151 | 210.1170 | ||
95.0224 | −462.5013 | −262.8799 | −260.5522 | 696.6406 | 689.8140 | ||
3.0727 | 16.2879 | 26.2879 | 17.3570 | 22.3570 | 18.0727 | ||
538.5282 | −446.6342 | −445.0099 | 209.2416 | ||||
−457.5883 | −265.6701 | −263.1652 | 699.7429 | ||||
17.6355 | 27.6355 | 20.7081 | 25.7081 | ||||
−439.5869 | 210.1170 | ||||||
−254.8022 | 689.8140 | ||||||
14.7198 | 19.7198 | ||||||
629.2236 | 0.0134 | 629.2727 | 0.0151 | 639.9965 | 0.0254 | 632.7031 | 0.1205 |
84.0842 | 0.0162 | 84.1765 | 0.0108 | 91.4590 | 0.0235 | 102.1926 | 0.1057 |
14.8946 | 0.0180 | 4.2584 | 0.0149 | 16.7208 | 0.0232 | 7.9921 | 0.1125 |
626.7046 | 0.0121 | 638.7539 | 0.0133 | 636.1058 | 0.0753 | 630.6615 | 0.0852 |
87.9179 | 0.0107 | 90.4175 | 0.0154 | 97.5176 | 0.0541 | 100.7179 | 0.0816 |
3.4601 | 0.0155 | 7.8524 | 0.0151 | 7.5773 | 0.0429 | 2.0002 | 0.0344 |
622.3238 | 0.0142 | 640.0670 | 0.0180 | 635.6114 | 0.0907 | 629.4778 | 0.0233 |
94.3276 | 0.0139 | 91.4741 | 0.0189 | 97.8561 | 0.0687 | 99.8918 | 0.0422 |
4.1908 | 0.0188 | 5.6040 | 0.0201 | 3.2677 | 0.0789 | 4.6397 | 0.0179 |
621.9169 | 0.0190 | 635.8859 | 0.0595 | 627.7632 | 0.0912 | ||
95.0236 | 0.0224 | 97.9292 | 0.0614 | 98.7844 | 0.0894 | ||
2.9661 | 0.0256 | 16.2601 | 0.0209 | 7.4481 | 0.0796 | ||
632.8328 | 0.1003 | 630.4326 | 0.0488 | ||||
102.1181 | 0.0982 | 100.3622 | 0.0198 | ||||
17.6777 | 0.1143 | 10.7676 | 0.0158 | ||||
622.0444 | 0.0129 | ||||||
94.8902 | 0.0164 | ||||||
4.7548 | 0.0246 |
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Marek, J.; Chmelař, P. Survey of Point Cloud Registration Methods and New Statistical Approach. Mathematics 2023, 11, 3564. https://doi.org/10.3390/math11163564
Marek J, Chmelař P. Survey of Point Cloud Registration Methods and New Statistical Approach. Mathematics. 2023; 11(16):3564. https://doi.org/10.3390/math11163564
Chicago/Turabian StyleMarek, Jaroslav, and Pavel Chmelař. 2023. "Survey of Point Cloud Registration Methods and New Statistical Approach" Mathematics 11, no. 16: 3564. https://doi.org/10.3390/math11163564