未知环境基于单目次优视差的多模滤波目标跟踪算法

doi:10.11772/j.issn.1001-9081.2018071535

计算机应用 ›› 2019, Vol. 39 ›› Issue (3): 864-868.DOI: 10.11772/j.issn.1001-9081.2018071535

• 虚拟现实与多媒体计算 • 上一篇下一篇

未知环境基于单目次优视差的多模滤波目标跟踪算法

黄帅, 付光远, 伍明, 岳敏

火箭军工程大学 301教研室, 西安 710025

收稿日期:2018-07-25 修回日期:2018-08-28 出版日期:2019-03-10 发布日期:2019-03-11
通讯作者: 付光远
作者简介:黄帅(1994-),男,湖南岳阳人,硕士研究生,主要研究方向:目标跟踪、单目SLAM;付光远(1965-),男,四川眉山人,教授,博士,主要研究方向:人工智能、指挥信息系统;伍明(1981-),男,陕西西安人,讲师,博士,主要研究方向:目标跟踪、单目SLAM;岳敏(1995-),女,陕西西安人,博士研究生,主要研究方向:目标跟踪、单目SLAM。
基金资助:
国家自然科学基金资助项目（61503389）；陕西省自然科学基金资助项目（2016JM6061）。

Multi-mode filtering object tracking algorithm based on monocular suboptimal parallax under unknown environment

HUANG Shuai, FU Guangyuan, WU Ming, YUE Min

Teaching and Research Section 301, Rocket Force University of Engineering, Xi'an Shaanxi 710025, China

Received:2018-07-25 Revised:2018-08-28 Online:2019-03-10 Published:2019-03-11
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61503389), the Natural Science Foundation of Shaanxi Province (2016JM6061).

摘要/Abstract

摘要：

在未知环境下基于单目视觉的机器人同时定位、地图构建和目标追踪的耦合问题（SLAMOT）中，需要足够的视差才能满足目标跟踪的可观性条件。同时，针对目标运动的不确定性以及系统对于目标运动方式的未知性，提出一种基于次优视差的多模滤波目标跟踪算法。首先，采用目标不确定性椭球投影面积变化最大的方向为次优视差方向，并将其作为机器人视差控制方向；然后，采用多模滤波算法计算目标各种运动方式的概率；其次，对各运动方式的目标状态进行估计，最后根据各运动方式的概率加权估计出目标状态。另外，考虑到工程应用中应减小能耗，因此，在满足目标跟踪要求的条件下，降低视差速度。仿真实验表明：视差速度为0.3 m/s时，次优视差算法的残差均值为0.16 m，而启发式算法、多模滤波算法、传统扩展卡尔曼滤波（EKF）算法的残差均值为0.25 m、0.06 m和0.16 m。在视差速度较低时，所提算法也能满足目标跟踪的可观性条件，具有较强的工程应用价值。

关键词: 目标跟踪, 同时定位与地图构建, 多模滤波, 次优视差, 控制策略

Abstract:

Under unknown environment, Simultaneous Localization, Mapping and Object Tracking (SLAMOT) based on monocular vision needs sufficient parallax to meet the observability condition of object tracking. Focused on the uncertainty of target motion and the unknown of the system on target motion mode, a multi-mode filtering target tracking algorithm based on monocular suboptimal parallax was proposed. Firstly, the direction in which the target uncertain ellipsoid projection area changed the most was selected as the suboptimal parallax direction and was used as the robot parallax control direction. Then, multi-mode filtering algorithm was used to calculate the probability of different motion modes of the target, estimating the target state of different motion modes. Finally, the target state was estimated according to the probabilistic weighting of each motion mode. The simulation results show that the residual error of suboptimal disparity algorithm is 0.16 m when the parallax velocity is 0.3 m/s, meanwhile the residual means of heuristic algorithm, multimode filtering algorithm, traditional Extended Kalman Filter (EKF) algorithm are 0.25 m, 0.06 m and 0.16 m respectively. Besides, when the parallax speed is small, the proposed algorithm also can satisfy the observability condition of target tracking, having important engineering application value.

Key words: object tracking, Simultaneous Localization And Mapping (SLAM), multi-mode filtering, suboptimal parallax, control strategy

中图分类号:

TP249

黄帅, 付光远, 伍明, 岳敏. 未知环境基于单目次优视差的多模滤波目标跟踪算法[J]. 计算机应用, 2019, 39(3): 864-868.

HUANG Shuai, FU Guangyuan, WU Ming, YUE Min. Multi-mode filtering object tracking algorithm based on monocular suboptimal parallax under unknown environment[J]. Journal of Computer Applications, 2019, 39(3): 864-868.

参考文献

[1] WANG C-C, THORPE C. Simultaneous localization and mapping with detection and tracking of moving objects[C]//Proceedings of the 2002 IEEE International Conference on Robotics and Automation. Piscataway, NJ:IEEE, 2002:2918-2924.
[2] WANG C-C, THORPE C, THRUN S, et al. Simultaneous localization, mapping and moving object tracking[J]. International Journal of Robotics Research, 2007, 26(9):889-916.
[3] WAN K W, WANG C-C, TON T T. Weakly interacting object tracking in indoor environments[C]//Proceedings of the 2008 IEEE Workshop on Advanced Robotics and Its Social Impacts. Piscataway, NJ:IEEE, 2008:1-6.
[4] WANG C-C, THORPE C, THRUN S. Online simultaneous localization and mapping with detection and tracking of moving objects:theory and results from a ground vehicle in crowded urban areas[C]//Proceedings of the 2003 IEEE International Conference on Robotics and Automation. Piscataway, NJ:IEEE, 2003,1:842-849.
[5] BAIG Q, VU T-D, AYCARD O. Online localization and mapping with moving objects detection in dynamic outdoor environments[C]//Proceedings of the 2009 IEEE 5th International Conference on Intelligent Computer Communication and Processing. Piscataway, NJ:IEEE, 2009:190-195.
[6] WANG C-C, LO T-C, YANG S-W. Interacting object tracking in crowded urban areas[C]//Proceedings of the 2007 IEEE International Conference on Robotics and Automation. Piscataway, NJ:IEEE, 2007:4626-4632.
[7] CIVERA J, DAVISON A J, MONTIEL J M M. Inverse depth parametrization for monocular SLAM[J]. IEEE Transactions on Robotics, 2008, 24(5):932-945.
[8] DAVISON A J. Real-time simultaneous localisation and mapping with a single camera[C]//ICCV'03:Proceedings of the 9th IEEE International Conference on Computer Vision. Washington, DC:IEEE Computer Society, 2003, 2:1403.
[9] DAVISON A J, REID I D, MOLTON N D, et al. MonoSLAM:real-time single camera SLAM[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(6):1052-1067.
[10] ESS A, LEIBE B, SCHINDLER K, et al. Robust multiperson tracking from a mobile platform[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(10):1831-1846.
[11] ESS A, LEIBE B, SCHINDLER K, et al. Moving obstacle detection in highly dynamic scenes[C]//Proceedings of the 2009 IEEE International Conference on Robotics and Automation. Piscataway, NJ:IEEE, 2009:56-63.
[12] ESS A, LEIBE B, SCHINDLER K, et al. A mobile vision system for robust multi-person tracking[C]//Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2008:1-8.
[13] CIVERA J, GRASA O G, DAVISON A J, et al. 1-point RANSAC for extended Kalman filtering:application to real-time structure from motion and visual odometry[J]. Journal of Field Robotics, 2010, 27(5):609-631.
[14] LI H-P, XU D-M, ZHANG F-B, et al. Consistency analysis of EKF-based SLAM by measurement noise and observation times[J]. Acta Automatica Sinica, 2009, 35(9):1177-1184.
[15] 梁明杰,闵华清,罗荣华.基于图优化的同时定位与地图创建综述[J].机器人,2013,35(4):500-512.(LIANG M J, MIN H Q, LUO R H. Graph-based SLAM:a survey[J]. Robot, 2013, 35(4):500-512.)

未知环境基于单目次优视差的多模滤波目标跟踪算法

Multi-mode filtering object tracking algorithm based on monocular suboptimal parallax under unknown environment

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	姬张建, 任兴旺. 带旋转与尺度估计的全卷积孪生网络目标跟踪算法[J]. 计算机应用, 2021, 41(9): 2705-2711.
[2]	钟莎, 黄玉清. 基于孪生区域候选网络的无人机指定目标跟踪[J]. 计算机应用, 2021, 41(2): 523-529.
[3]	张晓晗, 冯爱民. 基于经验模态分解和长短期记忆神经网络的短期交通流量预测[J]. 计算机应用, 2021, 41(1): 225-230.
[4]	张杰, 常天庆, 戴文君, 郭理彬, 张雷. 基于相关滤波与颜色概率模型的目标跟踪算法[J]. 计算机应用, 2020, 40(6): 1774-1782.
[5]	冯丽萍, 韩琦, 周志刚, 白增亮. 社交网络影响的不良信息扩散建模及最优控制策略[J]. 计算机应用, 2020, 40(3): 735-739.
[6]	何祥宇, 李静, 杨数强, 夏玉杰. 基于ET-PHD滤波器和变分贝叶斯近似的扩展目标跟踪算法[J]. 计算机应用, 2020, 40(12): 3701-3706.
[7]	赵宏, 刘向东, 杨永娟. 基于RGB-D图像的室内机器人同时定位与地图构建[J]. 计算机应用, 2020, 40(12): 3637-3643.
[8]	石国强, 赵霞. 基于联合优化的强耦合孪生区域推荐网络的目标跟踪算法[J]. 计算机应用, 2020, 40(10): 2822-2830.
[9]	杨博, 蔺素珍, 禄晓飞, 李大威, 秦品乐, 左健宏. 基于多特征融合与分层数据关联的空中红外多目标跟踪方法[J]. 计算机应用, 2020, 40(10): 3075-3080.
[10]	俞皓芳, 孙力帆, 付主木. 基于改进K-means++聚类的多扩展目标跟踪算法[J]. 计算机应用, 2020, 40(1): 271-277.
[11]	何祥宇, 于斌, 夏玉杰. 粒子概率假设密度平滑器异常平滑问题的解决方法[J]. 计算机应用, 2020, 40(1): 299-303.
[12]	贾永超, 何小卫, 郑忠龙. 融合重检测机制的卷积回归网络目标跟踪算法[J]. 计算机应用, 2019, 39(8): 2247-2251.
[13]	丁斗建, 赵晓林, 王长根, 高关根, 寇磊. 基于视觉的机器人自主定位与障碍物检测方法[J]. 计算机应用, 2019, 39(6): 1849-1854.
[14]	谭喜堂, 刘莎, 朱琴跃, 范清雯, 王晨. 矩阵式LED远光灯智能辅助控制系统[J]. 计算机应用, 2019, 39(6): 1855-1862.
[15]	韩玉兰, 韩崇昭. 非线性系统的多扩展目标跟踪算法[J]. 计算机应用, 2019, 39(5): 1318-1324.