Infrared and Laser Engineering, Volume. 50, Issue 12, 20210115(2021)
SVS-NLMS point cloud registration algorithm based on geometric algebra
Figures & Tables(9)
Fig. 2. Experiment results of cube data set simulation. (a) Raw data; (b) Result of SAC-IA+ICP algorithm registration; (c) Result of GA-SVSNLMS algorithm registration
Fig. 3. Convergence curves of each algorithm in geometric algebraic space of cube dataset. (a) Convergence curves of error function; (b) Convergence curves of cost function
Fig. 4. Experiment results of bunny data set simulation. (a) Raw data; (b) Result of SAC-IA+ICP algorithm registration; (c) Result of GA-SVSNLMS algorithm registration
Fig. 5. Convergence curves of each algorithm in geometric algebraic space of bunny dataset. (a) Error function curve and cost function curve of GA-SVSNLMS; (b) Cost function convergence curve of each algorithm in geometric algebraic space
Fig. 6. Simulation experiment results of each data set under Gaussian noise. (a) Raw data of cube; (b) Result of SAC-IA+ICP algorithm registration of cube; (c) Result of GA-SVSNLMS algorithm registration of cube; (d) Raw data of bunny; (e) Result of SAC-IA+ICP algorithm registration of bunny; (f) Result of GA-SVSNLMS algorithm registration of bunny
Table 1. Running accuracy and convergence speed of different algorithms
View tableView in ArticleTable 1. Running accuracy and convergence speed of different algorithms
Algorithm RMSE/mm Convergence speed/times ICP ${\rm{2}}.{\rm{5079}} \times {10^{ - 2}}$ 1000 SAC-IA+ICP ${\rm{2}}.2{\rm{483}} \times {10^{ - 2}}$ 1000 GA-LMS $1.2617 \times {10^{ - 8}}$ 750 GA-NLMS( $\;\beta = 0$ )$1.2684 \times {10^{ - 8}}$ 140 GA-NLMS( $\;\beta = {\rm{1}}$ )$1.2679 \times {10^{ - 8}}$ 175 GA-SVSNLMS $1.8852 \times {10^{ - 8}}$ 70
Table 2. Running accuracy and convergence speed of different algorithms
View tableView in ArticleTable 2. Running accuracy and convergence speed of different algorithms
Algorithm RMSE/mm Convergence speed/times ICP ${\rm{5}}.{\rm{4046}} \times {10^{ - {\rm{3}}}}$ 200 SAC-IA+ICP ${\rm{4}}.{\rm{4687}} \times {10^{ - {\rm{3}}}}$ 200 GA-LMS ${\rm{2}}{\rm{.9595}} \times {10^{ - {\rm{3}}}}$ 210 GA-NLMS( $\;\beta = 0$ )${\rm{2}}{\rm{.9443}} \times {10^{ - {\rm{3}}}}$ 55 GA-NLMS( $\;\beta = {\rm{1}}$ )${\rm{2}}{\rm{.7620}} \times {10^{ - {\rm{3}}}}$ 85 GA-SVSNLMS ${\rm{2}}{\rm{.6658}} \times {10^{ - {\rm{3}}}}$ 40
Table 3. Running accuracy and convergence speed of different algorithms under Gaussian noise
View tableView in ArticleTable 3. Running accuracy and convergence speed of different algorithms under Gaussian noise
Algorithm cube bunny RMSE/mm Convergence speed/times RMSE/mm Convergence speed/times ICP ${\rm{2}}.{\rm{284\;7}} \times {10^{ - {\rm{1}}}}$ 1000 ${\rm{1}}{\rm{.286\;4}} \times {10^{ - 2}}$ 200 SAC-IA+ICP ${\rm{3}}{\rm{.328\;6}} \times {10^{ - 2}}$ 1000 ${\rm{6}}{\rm{.398\;4}} \times {10^{ - {\rm{3}}}}$ 200 GA-LMS ${\rm{5}}{\rm{.227\;6}} \times {10^{ - {\rm{3}}}}$ 750 ${\rm{6}}{\rm{.015\;9}} \times {10^{ - {\rm{3}}}}$ 210 GA-NLMS( $\;\beta = 0$ )${\rm{5}}{\rm{.603\;7}} \times {10^{ - {\rm{3}}}}$ 140 ${\rm{6}}{\rm{.019\;4}} \times {10^{ - {\rm{3}}}}$ 55 GA-NLMS( $\;\beta = {\rm{1}}$ )${\rm{5}}{\rm{.576\;8}} \times {10^{ - {\rm{3}}}}$ 175 ${\rm{6}}{\rm{.005\;6}} \times {10^{ - {\rm{3}}}}$ 85 GA-SVSNLMS ${\rm{5}}{\rm{.237\;7}} \times {10^{ - {\rm{3}}}}$ 70 ${\rm{5}}{\rm{.841\;5}} \times {10^{ - {\rm{3}}}}$ 40
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Wentao Cui, Weidong Jiao, Yanli Pang. SVS-NLMS point cloud registration algorithm based on geometric algebra[J]. Infrared and Laser Engineering, 2021, 50(12): 20210115
Paper Information
Category: Image processing
Received: Feb. 24, 2021
Accepted: --
Published Online: Feb. 9, 2022
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