Research on Laser SLAM Algorithm Based on Sparse Pose Optimization

Parameter	Gmapping	Hector	Proposed algorithm
Max error /m	0.0790	0.09300	0.0770
Min error /m	0.0250	0.03200	0.0280
Mean square error(MSE) /m²	0.0023	0.01870	0.0023
Root mean square error(RMSE) /m	0.0482	0.13697	0.0481
Mean absolute error(MAE) /m	0.0443	0.05140	0.0452

Table 2. Error analysis of the three algorithms in complex laboratory environment
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Table 2. Error analysis of the three algorithms in complex laboratory environment
Parameter Gmapping Hector Proposed algorithm
Max error /m 0.0310 0.0520 0.0250
Min error /m 0.0170 0.0240 0.0130
MSE /m² 0.0005 0.0011 0.0003
RMSE /m 0.0231 0.0343 0.0194
MAE /m 0.0227 0.0329 0.0190

Table 3. CPU load of the three algorithms
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Table 3. CPU load of the three algorithms
Parameter Gmapping Hector Proposed algorithm
Mean CPU load /% 7.0738 6.1213 5.3897
Median CPU load /% 5.6100 5.9310 5.3010
Standard deviation of CPU load /% 4.4312 1.987 1.310

Table 4. Running time of the three algorithms unit: s
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Table 4. Running time of the three algorithms unit: s
Environment Gmapping Hector Proposed algorithm
Corridor 25.81 28.64 24.40
Laboratory 105.92 120.48 98.04

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Dong Shen, Yuhang Xu, Qiang Li, Jing Di, Xia Huang. Research on Laser SLAM Algorithm Based on Sparse Pose Optimization[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2428003

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