Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (10): 3246-3251.DOI: 10.11772/j.issn.1001-9081.2023101389
• Frontier and comprehensive applications • Previous Articles Next Articles
Enbao QIAO1,2, Xiangyang GAO2, Jun CHENG2()
Received:
2023-10-16
Revised:
2023-12-27
Accepted:
2024-01-08
Online:
2024-10-15
Published:
2024-10-10
Contact:
Jun CHENG
About author:
QIAO Enbao, born in 1996, M. S. candidate. His research interests include intelligent robots, embedded systems.Supported by:
通讯作者:
程俊
作者简介:
乔恩保(1996—),男,安徽阜阳人,硕士研究生,CCF会员,主要研究方向:智能机器人、嵌入式系统基金资助:
CLC Number:
Enbao QIAO, Xiangyang GAO, Jun CHENG. Self-recovery adaptive Monte Carlo localization algorithm based on support vector machine[J]. Journal of Computer Applications, 2024, 44(10): 3246-3251.
乔恩保, 高向阳, 程俊. 基于支持向量机的自恢复自适应蒙特卡洛定位算法[J]. 《计算机应用》唯一官方网站, 2024, 44(10): 3246-3251.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023101389
参数名 | 值 | 参数名 | 值 |
---|---|---|---|
粒子数最小值 | 500 | 雷达最小测量距离 | 0.2 m |
粒子数最大值 | 5 000 | 雷达最大测量距离 | 16 m |
触发更新最小距离 | 0.05 m | 雷达扫描角度 | 360° |
触发更新最小角度 | 0.2 rad | 雷达采样频率 | 16 kHz |
Tab. 1 Parameter setting
参数名 | 值 | 参数名 | 值 |
---|---|---|---|
粒子数最小值 | 500 | 雷达最小测量距离 | 0.2 m |
粒子数最大值 | 5 000 | 雷达最大测量距离 | 16 m |
触发更新最小距离 | 0.05 m | 雷达扫描角度 | 360° |
触发更新最小角度 | 0.2 rad | 雷达采样频率 | 16 kHz |
SVM类型 | 核函数类型 | 成功率 |
---|---|---|
C-SVC | RBF | 95.19 |
线性核函数 | 94.38 | |
V-SVC | RBF | 94.58 |
线性核函数 | 83.96 |
Tab. 2 SVM model test results
SVM类型 | 核函数类型 | 成功率 |
---|---|---|
C-SVC | RBF | 95.19 |
线性核函数 | 94.38 | |
V-SVC | RBF | 94.58 |
线性核函数 | 83.96 |
参数名 | 值 | 参数名 | 值 |
---|---|---|---|
SVM类型 | C-SVC | 支持向量总数 | 156 |
核函数类型 | RBF | 超平面的偏移值 | -0.992 |
Gamma | 0.125 | 每个类别支持向量数 | 78 |
数据种类数 | 2 |
Tab. 3 Parameter setting of SVM model
参数名 | 值 | 参数名 | 值 |
---|---|---|---|
SVM类型 | C-SVC | 支持向量总数 | 156 |
核函数类型 | RBF | 超平面的偏移值 | -0.992 |
Gamma | 0.125 | 每个类别支持向量数 | 78 |
数据种类数 | 2 |
模型 | 绑架时检测成功率 | 整体误检率 |
---|---|---|
PRM[ | 85 | 7.5 |
SVM-KDM(本文) | 95 | 2.5 |
Tab. 4 Success rates and false detection rates of two detection models
模型 | 绑架时检测成功率 | 整体误检率 |
---|---|---|
PRM[ | 85 | 7.5 |
SVM-KDM(本文) | 95 | 2.5 |
算法 | 恢复定位所需更新次数 | 定位误差/m | |||
---|---|---|---|---|---|
短距离绑架 | 长距离绑架 | 平均值 | 最大值 | 最小值 | |
AMCL[ | 24 | 恢复失败 | 1.558 0 | 4.482 0 | 0.012 4 |
SR-MCL[ | 1 | 1 | 0.104 7 | 4.105 0 | 0.003 0 |
SVM-SRAMCL | 1 | 1 | 0.069 2 | 4.220 0 | 0.003 0 |
Tab. 5 Simulation results of different algorithms
算法 | 恢复定位所需更新次数 | 定位误差/m | |||
---|---|---|---|---|---|
短距离绑架 | 长距离绑架 | 平均值 | 最大值 | 最小值 | |
AMCL[ | 24 | 恢复失败 | 1.558 0 | 4.482 0 | 0.012 4 |
SR-MCL[ | 1 | 1 | 0.104 7 | 4.105 0 | 0.003 0 |
SVM-SRAMCL | 1 | 1 | 0.069 2 | 4.220 0 | 0.003 0 |
算法 | 自恢复 成功率/% | 绑架检测 成功率/% | 成功所需平均 更新次数 |
---|---|---|---|
AMCL[ | 25 | 26.0 | |
SR-MCL[ | 85 | 90 | 9.5 |
SVM-SRAMC | 88 | 96 | 5.4 |
Tab. 6 Experimental results in real environment
算法 | 自恢复 成功率/% | 绑架检测 成功率/% | 成功所需平均 更新次数 |
---|---|---|---|
AMCL[ | 25 | 26.0 | |
SR-MCL[ | 85 | 90 | 9.5 |
SVM-SRAMC | 88 | 96 | 5.4 |
1 | 杨傲雷,金宏宙,陈灵,等.融合深度学习与粒子滤波的移动机器人重定位方法[J].仪器仪表学报,2021,42(7):226-233. |
YANG A L, JIN H Z, CHEN L, et al. Mobile robot relocalization method fusing deep learning and particle filtering [J]. Journal of Scientific Instrument, 2021, 42(7): 226-233. | |
2 | CAI G-S, LIN H-Y, S-F KAO. Mobile robot localization using GPS, IMU and visual odometry [C]// Proceedings of the 2019 International Automatic Control Conference. Piscataway: IEEE, 2019: 1-6. |
3 | 秦正泓,刘冉,肖宇峰,等.基于WiFi指纹序列匹配的机器人同步定位与地图构建[J].计算机应用,2022,42(10):3268-3274. |
QIN Z H, LIU R, XIAO Y F, et al. Simultaneous localization and mapping for mobile robot based on WiFi fingerprint sequence matching [J]. Journal of Computer Applications, 2022, 42(10): 3268-3274. | |
4 | NILWONG S, HOSSAIN D, KANEKO S I, et al. Deep learning-based landmark detection for mobile robot outdoor localization [J]. Machines, 2019, 7(2): 25. |
5 | LIN R, DONG S, ZHAO W, et al. Ultra-wide-band-based adaptive Monte Carlo localization for kidnap recovery of mobile robot [J]. International Journal of Advanced Robotic Systems, 2023, 20(2): 1-13. |
6 | ANDRADI H, BLUMENTHAL S, PRASSLER E, et al. LiDAR-based indoor localization with optimal particle filters using surface normal constraint [C]// Proceedings of the 2023 IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2023: 1947-1953. |
7 | 周瑞,鲁翔,卢帅,等.基于粒子滤波和地图匹配的融合室内定位[J].电子科技大学学报,2018,47(3):415-420. |
ZHOU R, LU X, LU S, et al. Fused indoor localization based on particle filtering and map matching [J]. Journal of University of Electronic Science and Technology of China, 2018, 47(3): 415-420. | |
8 | TIAN Y, LIU F, LIU H, et al. A real-time and fast LiDAR-IMU-GNSS SLAM system with point cloud semantic graph descriptor loop-closure detection [J]. Advanced Intelligent Systems, 2023, 5(10):2300138. |
9 | THRUN S, FOX D, BURGARD W, et al. Robust Monte Carlo localization for mobile robots[J]. Artificial Intelligence, 2001, 128(1/2): 99-141. |
10 | M-A CHUNG, LIN C-W. An improved localization of mobile robotic system based on AMCL algorithm [J]. IEEE Sensors Journal, 2022, 22(1): 900-908. |
11 | 胡章芳,曾林全,罗元,等.融入二维码信息的自适应蒙特卡洛定位算法[J].计算机应用,2019,39(4):989-993. |
HU Z F, ZENG L Q, LUO Y, et al. Adaptive Monte-Carlo localization algorithm integrated with two-dimensional code information [J]. Journal of Computer Applications, 2019, 39(4): 989-993. | |
12 | 李忠发,杨光,马磊,等.变电站巡检机器人重定位研究[J].计算机科学,2020,47(6A):599-602. |
LI Z F, YANG G, MA L, et al. Research on relocation of substation inspection robot [J].Computer Science, 2020, 47(6A): 599-602. | |
13 | PARK S, ROH K S. Coarse-to-fine localization for a mobile robot based on place learning with a 2-D range scan [J]. IEEE Transactions on Robotics, 2016, 32(3): 528-544. |
14 | XU S, CHOU W, DONG H. A robust indoor localization system integrating visual localization aided by CNN-based image retrieval with Monte Carlo localization [J]. Sensors, 2019, 19(2): 249. |
15 | 蒋林,聂文康,朱建阳,等.基于具有墙角信息的语义地图改进AMCL重定位算法[J].机械工程学报,2023,58(24):312-323. |
JIANG L, NIE W K, ZHU J Y, et al. Improved AMCL relocation algorithm based on semantic map with corner information [J]. Journal of Mechanical Engineering, 2023, 58(24): 312-323. | |
16 | TIAN Y, MA S. Kidnapping detection and recognition in previous unknown environment [J]. Journal of Sensors, 2017, 2017: 6468427. |
17 | CHENG Y, LIN R, ZHAO W. Laser vision for low-coupling indoor positioning [C]// Proceedings of the 2023 IEEE International Conference on Mechatronics and Automation. Piscataway: IEEE, 2023: 2163-2170. |
18 | 陈铭,张淑芳,缪长蔚,等.自恢复蒙特卡罗定位算法[J]. 光电子·激光,2023,34(1):43-51. |
CHEN M, ZHANG S F, MIAO C W, et al. Self-recovery Monte Carlo localization algorithm [J]. Journal of Optoelectronics·Laser, 2023, 34(1): 43-51. | |
19 | 袁千贺,田昕,沈斯杰.基于多传感器融合的移动机器人定位[J].计算机系统应用,2022,31(3):136-142. |
YUAN Q H, TIAN X, SHEN S J. Mobile robot localization based on multi-sensor fusion [J]. Computer Systems & Applications, 2022, 31(3): 136-142. | |
20 | JOON A, KOWALCZYK W. Leader following control of non-holonomic mobile robots using EKF-based localization [C]// Proceedings of the 2023 27th International Conference on Methods and Models in Automation and Robotics. Piscataway: IEEE, 2023: 57-62. |
21 | YU L, LI M, PAN G. Indoor localization based on fusion of AprilTag and adaptive Monte Carlo [C]// Proceedings of the 2021 IEEE 5th Information Technology, Networking, Electronic and Automation Control Conference. Piscataway: IEEE, 2021: 464-468. |
22 | GARROTE L, BARROS T, PEREIRA R, et al. Absolute indoor positioning-aided laser-based particle filter localization with a refinement stage [C]// Proceedings of the 2019 Annual Conference of the IEEE Industrial Electronics Society. Piscataway: IEEE, 2019: 597-603. |
23 | 李航.统计学习方法[M].北京:清华大学出版社,2012:111-147. |
LI H. Statistical Learning Methods [M]. Beijing: Tsinghua University Press, 2012: 111-147. | |
24 | HU C, DONG W, YANG Y, et al. Runtime verification on hierarchical properties of ROS-based robot swarms [J]. IEEE Transactions on Reliability, 2020, 69(2): 674-689. |
25 | GUAN R, RISTIC B, WANG L, et al. KLD sampling with Gmapping proposal for Monte Carlo localization of mobile robots [J]. Information Fusion, 2019, 49: 79-88. |
26 | CHANG C C, LIN C J. LIBSVM: a library for support vector machines [CP/OL]. (2019-12-10)[2023-10-01]. . |
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