家政服务机器人同时定位与地图构建研究
|
摘要
随着社会老龄化程度的不断提高,以及现代人生活方式的转变,家政服务机器人正逐渐走入人们的生活,并且显示出巨大的市场需求。
家政服务机器人要投入实际应用,自主导航能力是关键,也是实现其真正智能化和完全自主移动的关键技术。而家政服务机器人定位和环境地图的构建是实现导航的基础。定位过程依赖于精确的地图信息,而地图构建又反过来依赖于精确的位姿信息。二者既矛盾又相关,必须同时加以考虑。因此,本文的研究内容为家政服务机器人同时定位与地图构建(SLAM)。
本文首先对基于距离传感器的室内环境下的同时定位与地图构建进行研究。针对传统的基于Rao-Blackwellized粒子滤波器的SLAM算法需要大量的采样粒子,而且频繁重采样操作可能导致粒子耗尽的问题,提出并实现了一种改进算法。改进的Rao-Blacekwellized粒子滤波SLAM算法在计算采样的提议分布时考虑了里程计信息和距离传感器信息,并且通过计算有效粒子数目适时进行重采样操作,通过加入随机粒子来维持多样性。该方法能减少粒子数目,同时保证算法的一致性。仿真实验证明,改进的算法提高了计算效率,创建的栅格地图具有更高的精度。
其次,本文研究了基于视觉的家政服务机器人同时定位与地图构建(vSLAM)。vSLAM的关键点之一是视觉图像特征点的提取。本文创新性地将快速鲁棒特征(SURF)算法应用到家政服务机器人环境认知中,实现了基于单目视觉的SURF地图库图像匹配。机器人从成功匹配的图像中获得当前所处的环境信息。SURF算法比尺度不变特征变换(SIFT)算法的计算速度提高了3倍,因此具有更好的实时性。家政服务机器人获得了一定的环境认知后,同时结合距离传感器和里程计创建的室内环境地图,即可以生成包含语义信息的地图。有助于家政服务机器人完成路径规划、导航等后续任务。
With the improvement of aging society and the change of modern lifestyle, home service robots graduately enter people's lives, showing the huge market demand.
In order to make home service robots practical application, the ability of autonomous navigation is the key issue, which is also the key technology to realize the real intelligence and mobility. Simultaneous Localization and Mapping (SLAM) of home service robot is the basis for navigation. On the one hand, localization relies on the accurate map information. On the other hand, map construction, in turn, depends on the precise pose information. They are contradict and related, thus must be considered simultaneously. Therefore, this dissertation researches on the SLAM problem of home service robots.
This paper firstly studies the SLAM problem based on the range sensors. To due with the problem that the conventional Rao-Blackwellized particle filters based SLAM algorithm requires a large number of particles and that the frequent resampling might lead to the problem of particle impoverishment,an improved approach is proposed.It takes into account both the odometry and the observed information when computing the proposal distribution, resample according to the calculation of the effective sample number and adds some stochastic particles in order to maintain the diversity.Thus this novel method decreases the number of particles and is able to meet the requirement of consistence. Stimulation experimental results show that the proposed algorithm improves the computational performance as well as builds grid maps with higher accuracy.
Secondly, this paper does researches on visual SLAM (vSLAM) problem for home service robots. One of the key issues in vSLAM is feature points extraction of visual images. In this paper, the Speeded Up Robust Features (SURF) algorithm is employed to solve the environment recognize problem of the home service robot, and the library based image matching using only monocular vision is realized. The robot learns about the environmental information from successfully image matching. The SURF algorithm is three times faster in calculating speed than the Scale Invariant Feature Transform (SIFT) algorithm, so it is better in real time performance. After recognizing the environment, the home service robot combines the visual information and the grid map constructed from range sensors and odometer, so as to generate an indoor environment map containing semantic information. This map is helpful for home service robots to achieve follow-up tasks, such as path planning, navigation, etc.
家政服务机器人要投入实际应用,自主导航能力是关键,也是实现其真正智能化和完全自主移动的关键技术。而家政服务机器人定位和环境地图的构建是实现导航的基础。定位过程依赖于精确的地图信息,而地图构建又反过来依赖于精确的位姿信息。二者既矛盾又相关,必须同时加以考虑。因此,本文的研究内容为家政服务机器人同时定位与地图构建(SLAM)。
本文首先对基于距离传感器的室内环境下的同时定位与地图构建进行研究。针对传统的基于Rao-Blackwellized粒子滤波器的SLAM算法需要大量的采样粒子,而且频繁重采样操作可能导致粒子耗尽的问题,提出并实现了一种改进算法。改进的Rao-Blacekwellized粒子滤波SLAM算法在计算采样的提议分布时考虑了里程计信息和距离传感器信息,并且通过计算有效粒子数目适时进行重采样操作,通过加入随机粒子来维持多样性。该方法能减少粒子数目,同时保证算法的一致性。仿真实验证明,改进的算法提高了计算效率,创建的栅格地图具有更高的精度。
其次,本文研究了基于视觉的家政服务机器人同时定位与地图构建(vSLAM)。vSLAM的关键点之一是视觉图像特征点的提取。本文创新性地将快速鲁棒特征(SURF)算法应用到家政服务机器人环境认知中,实现了基于单目视觉的SURF地图库图像匹配。机器人从成功匹配的图像中获得当前所处的环境信息。SURF算法比尺度不变特征变换(SIFT)算法的计算速度提高了3倍,因此具有更好的实时性。家政服务机器人获得了一定的环境认知后,同时结合距离传感器和里程计创建的室内环境地图,即可以生成包含语义信息的地图。有助于家政服务机器人完成路径规划、导航等后续任务。
With the improvement of aging society and the change of modern lifestyle, home service robots graduately enter people's lives, showing the huge market demand.
In order to make home service robots practical application, the ability of autonomous navigation is the key issue, which is also the key technology to realize the real intelligence and mobility. Simultaneous Localization and Mapping (SLAM) of home service robot is the basis for navigation. On the one hand, localization relies on the accurate map information. On the other hand, map construction, in turn, depends on the precise pose information. They are contradict and related, thus must be considered simultaneously. Therefore, this dissertation researches on the SLAM problem of home service robots.
This paper firstly studies the SLAM problem based on the range sensors. To due with the problem that the conventional Rao-Blackwellized particle filters based SLAM algorithm requires a large number of particles and that the frequent resampling might lead to the problem of particle impoverishment,an improved approach is proposed.It takes into account both the odometry and the observed information when computing the proposal distribution, resample according to the calculation of the effective sample number and adds some stochastic particles in order to maintain the diversity.Thus this novel method decreases the number of particles and is able to meet the requirement of consistence. Stimulation experimental results show that the proposed algorithm improves the computational performance as well as builds grid maps with higher accuracy.
Secondly, this paper does researches on visual SLAM (vSLAM) problem for home service robots. One of the key issues in vSLAM is feature points extraction of visual images. In this paper, the Speeded Up Robust Features (SURF) algorithm is employed to solve the environment recognize problem of the home service robot, and the library based image matching using only monocular vision is realized. The robot learns about the environmental information from successfully image matching. The SURF algorithm is three times faster in calculating speed than the Scale Invariant Feature Transform (SIFT) algorithm, so it is better in real time performance. After recognizing the environment, the home service robot combines the visual information and the grid map constructed from range sensors and odometer, so as to generate an indoor environment map containing semantic information. This map is helpful for home service robots to achieve follow-up tasks, such as path planning, navigation, etc.
引文
[1]肖南峰.智能机器人[M].华南理工大学出版社, 2008
[2]禹建丽,张宗伟.自主移动服务机器人的研究现状浅析[J].中原工学院学报, 2008(04)
[3]徐方,张希伟,杜振军.我国家庭服务机器人产业发展现状调研报告[J].机器人技术与应用, 2009(02)
[4]张路金.移动机器人同时定位与地图创建研究[D].湖南大学, 2009
[5] Durrant-Whyte H., Bailey T. Simultaneous localization and mapping: Part I [J]. IEEE ROBOTICS & AUTOMATION MAGAZINE, 2006, 13(2): 99-108
[6] Smith R., Self M., Cheeseman P. Estimating uncertain spatial relationships in robotics [J]. Autonomous robot vehicles, 1990, 1: 167-193
[7] Dissanayake M., Newman P., Durrant-Whyte H.et al. An experimental and theoretical investigation into simultaneous localisation and map building [J]. Experimental Robotics VI, 2000: 265-274
[8] Guivant J.E., Nebot E.M. Optimization of the simultaneous localization and map-buildingalgorithm for real-time implementation [J]. IEEE Transactions on Robotics and Automation, 2001, 17(3): 242-257
[9] Leonard J.J., Feder H. A computationally efficient method for large-scale concurrent mapping and localization [A]. Robotics Research International Symposium [C], 2000: 169-178
[10] Thrun S., Burgard W., Fox D. A probabilistic approach to concurrent mapping and localization for mobile robots [J]. Autonomous Robots, 1998, 5(3): 253-271
[11] Doucet A., de Freitas N., Murphy K.et al. Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks [A]. Proceedings of the 16th Annual Conference on Uncertainty in Artificial Intelligence (UAI) [C]. San Francisco, CA, 2000: 171-176
[12] Murphy K. Bayesian map learning in dynamic environments [J]. Advances in Neural Information Processing Systems, 2000, 12: 1015-1021
[13] Montemerlo M., Thrun S., Koller D.et al. FastSLAM: A factored solution to the simultaneous localization and mapping problem [A]. Proceedings of the National conference on Artificial Intelligence [C], 2002: 593-598
[14] Grisetti G., Stachniss C., Burgard W. Improved techniques for grid mapping with rao-blackwellized particle filters [J]. IEEE Transactions on Robotics, 2007, 23(1): 34-46
[15] Davison A.J. Mobile robot navigation using active vision [J]. , 1998
[16] Krim S.A., Elaziz O., Chatila R. A computationally efficient EKF-vSLAM [A]. Control and Automation, 2008 16th Mediterranean Conference on [C], 2008: 511-516
[17] Murray D., Little J.J. Using real-time stereo vision for mobile robot navigation [J]. Autonomous Robots, 2000, 8(2): 161-171
[18] Se S., Lowe D.G., Little J.J. Vision-based global localization and mapping for mobile robots [J]. IEEE Transactions on robotics, 2005, 21(3): 364-375
[19] Se S., Lowe D., Little J. Vision-based mobile robot localization and mapping using scale-invariant features [A]. IEEE International Conference on Robotics and Automation [C], 2001: 2051-2058
[20] Kim J., Yoon K.J., Kim J.S.et al. Visual SLAM by Single-Camera Catadioptric Stereo [A]. SICE-ICASE, 2006. International Joint Conference [C], 2006: 2005-2009
[21] Karlsson N., di Bernardo E., Ostrowski J.et al. The vSLAM Algorithm for Robust Localization and Mapping [A]. Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on [C], 2005: 24-29
[22] Thrun S., Burgard W., Fox D. Probabilistic Robotics [M]. MIT Press, 2005
[23] Moravec H., Elfes A. High resolution maps from wide angle sonar [A]. Robotics and Automation. Proceedings. 1985 IEEE International Conference on [C], 1985: 116-121
[24] Kuipers B., Byun Y.T. A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations [J]. Robotics and Autonomous Systems, 1991, 8(1-2): 47-63
[25] Choset H., Burdick J. Sensor based planning. I. The generalized Voronoi graph [A]. Robotics and Automation, 1995. Proceedings., 1995 IEEE International Conference on [C], 1995: 1649-1655
[26]徐则中.移动机器人的同时定位和地图构建[D].浙江大学, 2004
[27]殷波.移动机器人同时定位与地图创建方法研究[D].中国海洋大学, 2006
[28] Jensfelt P., Christensen H.I. Pose tracking using laser scanning and minimalistic environmental models [J]. Robotics and Automation, IEEE Transactions on, 2001, 17(2): 138-147
[29]罗荣华洪炳镕.移动机器人同时定位与地图创建研究进展[J].机器人JQRR, 2004(02)
[30] Kwok C., Fox D., Meila M. Adaptive real-time particle filters for robot localization [A]. Proceedings of IEEE International Conference on Robotics and Automation [C], 2003: 2836-2841
[31] Thrun S., Fox D., Burgard W.et al. Robust Monte Carlo localization for mobile robots [J]. Artificial Intelligence, 2001, 128(1-2): 99-141
[32]郭剑辉,赵春霞.一种新的粒子滤波SLAM算法[J].计算机研究与发展, 2008, 45(005): 853-860
[33] Dissanayake M., Newman P., Clark S.et al. A solution to the simultaneous localization and map building (SLAM) problem [J]. IEEE Transactions on Robotics and Automation, 2001, 17(3): 229-241
[34]武二永.基于视觉的机器人同时定位与地图构建[D].浙江大学, 2007
[35]许俊勇.移动机器人全景vSLAM研究[D].上海交通大学, 2008
[36]王海军.未知环境下移动机器人即时定位与地图创建[D].上海大学, 2009
[37] Harris C., Stephens M. A combined corner and edge detector [A]. Alvey vision conference [C], 1988: 50
[38] Shi J., Tomasi C. Good features to track [A]. 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994. Proceedings CVPR'94. [C], 1994: 593-600
[39] Lowe D.G. Distinctive image features from scale-invariant keypoints [J]. International journal of computer vision, 2004, 60(2): 91-110
[40] Lindeberg T. Scale-space theory in computer vision [M]. Springer, 1993
[41] Koenderink J.J. The structure of images [J]. Biological cybernetics, 1984, 50(5): 363-370
[42] Friedman J.H., Bentley J.L., Finkel R.A. An algorithm for finding best matches inlogarithmic expected time [J]. ACM Transactions on Mathematical Software (TOMS), 1977, 3(3): 209-226
[43] Arya S., Mount D.M., Netanyahu N.S.et al. An optimal algorithm for approximate nearest neighbor searching fixed dimensions [J]. Journal of the ACM (JACM), 1998, 45(6): 891-923
[44] Bay H., Tuytelaars T., Van Gool L. Surf: Speeded up robust features [J]. Computer Vision–ECCV 2006, 2006: 404-417
[45]孙传昱.基于前向视觉的机器人自定位及环境认知[D].大连理工大学, 2008
[46] Lindeberg T. Feature detection with automatic scale selection [J]. International Journal of Computer Vision, 1998, 30(2): 79-116
[47] Mikolajczyk K., Schmid C. Indexing based on scale invariant interest points [A]. Proc. ICCV [C], 2001: 525-531
[48] Mozosó., Gil A., Ballesta M.et al. Interest point detectors for visual slam [J]. Current Topics in Artificial Intelligence, 2007: 170-179
[49] Gil A., Mozos O.M., Ballesta M.et al. A comparative evaluation of interest point detectors and local descriptors for visual slam [J]. Machine Vision and Applications, 2009: 1-16
[50] Murillo A.C., Guerrero J.J., Sagues C. Surf features for efficient robot localization with omnidirectional images [A]. 2007 IEEE International Conference on Robotics and Automation [C], 2007: 3901-3907
[51]陈卫东,张飞.移动机器人的同步自定位与地图创建研究进展[J].控制理论与应用, 2005, 22(003): 455-460
[52]汤晓.基于激光测距仪的移动机器人同时定位与地图创建[D].山东大学, 2007
[2]禹建丽,张宗伟.自主移动服务机器人的研究现状浅析[J].中原工学院学报, 2008(04)
[3]徐方,张希伟,杜振军.我国家庭服务机器人产业发展现状调研报告[J].机器人技术与应用, 2009(02)
[4]张路金.移动机器人同时定位与地图创建研究[D].湖南大学, 2009
[5] Durrant-Whyte H., Bailey T. Simultaneous localization and mapping: Part I [J]. IEEE ROBOTICS & AUTOMATION MAGAZINE, 2006, 13(2): 99-108
[6] Smith R., Self M., Cheeseman P. Estimating uncertain spatial relationships in robotics [J]. Autonomous robot vehicles, 1990, 1: 167-193
[7] Dissanayake M., Newman P., Durrant-Whyte H.et al. An experimental and theoretical investigation into simultaneous localisation and map building [J]. Experimental Robotics VI, 2000: 265-274
[8] Guivant J.E., Nebot E.M. Optimization of the simultaneous localization and map-buildingalgorithm for real-time implementation [J]. IEEE Transactions on Robotics and Automation, 2001, 17(3): 242-257
[9] Leonard J.J., Feder H. A computationally efficient method for large-scale concurrent mapping and localization [A]. Robotics Research International Symposium [C], 2000: 169-178
[10] Thrun S., Burgard W., Fox D. A probabilistic approach to concurrent mapping and localization for mobile robots [J]. Autonomous Robots, 1998, 5(3): 253-271
[11] Doucet A., de Freitas N., Murphy K.et al. Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks [A]. Proceedings of the 16th Annual Conference on Uncertainty in Artificial Intelligence (UAI) [C]. San Francisco, CA, 2000: 171-176
[12] Murphy K. Bayesian map learning in dynamic environments [J]. Advances in Neural Information Processing Systems, 2000, 12: 1015-1021
[13] Montemerlo M., Thrun S., Koller D.et al. FastSLAM: A factored solution to the simultaneous localization and mapping problem [A]. Proceedings of the National conference on Artificial Intelligence [C], 2002: 593-598
[14] Grisetti G., Stachniss C., Burgard W. Improved techniques for grid mapping with rao-blackwellized particle filters [J]. IEEE Transactions on Robotics, 2007, 23(1): 34-46
[15] Davison A.J. Mobile robot navigation using active vision [J]. , 1998
[16] Krim S.A., Elaziz O., Chatila R. A computationally efficient EKF-vSLAM [A]. Control and Automation, 2008 16th Mediterranean Conference on [C], 2008: 511-516
[17] Murray D., Little J.J. Using real-time stereo vision for mobile robot navigation [J]. Autonomous Robots, 2000, 8(2): 161-171
[18] Se S., Lowe D.G., Little J.J. Vision-based global localization and mapping for mobile robots [J]. IEEE Transactions on robotics, 2005, 21(3): 364-375
[19] Se S., Lowe D., Little J. Vision-based mobile robot localization and mapping using scale-invariant features [A]. IEEE International Conference on Robotics and Automation [C], 2001: 2051-2058
[20] Kim J., Yoon K.J., Kim J.S.et al. Visual SLAM by Single-Camera Catadioptric Stereo [A]. SICE-ICASE, 2006. International Joint Conference [C], 2006: 2005-2009
[21] Karlsson N., di Bernardo E., Ostrowski J.et al. The vSLAM Algorithm for Robust Localization and Mapping [A]. Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on [C], 2005: 24-29
[22] Thrun S., Burgard W., Fox D. Probabilistic Robotics [M]. MIT Press, 2005
[23] Moravec H., Elfes A. High resolution maps from wide angle sonar [A]. Robotics and Automation. Proceedings. 1985 IEEE International Conference on [C], 1985: 116-121
[24] Kuipers B., Byun Y.T. A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations [J]. Robotics and Autonomous Systems, 1991, 8(1-2): 47-63
[25] Choset H., Burdick J. Sensor based planning. I. The generalized Voronoi graph [A]. Robotics and Automation, 1995. Proceedings., 1995 IEEE International Conference on [C], 1995: 1649-1655
[26]徐则中.移动机器人的同时定位和地图构建[D].浙江大学, 2004
[27]殷波.移动机器人同时定位与地图创建方法研究[D].中国海洋大学, 2006
[28] Jensfelt P., Christensen H.I. Pose tracking using laser scanning and minimalistic environmental models [J]. Robotics and Automation, IEEE Transactions on, 2001, 17(2): 138-147
[29]罗荣华洪炳镕.移动机器人同时定位与地图创建研究进展[J].机器人JQRR, 2004(02)
[30] Kwok C., Fox D., Meila M. Adaptive real-time particle filters for robot localization [A]. Proceedings of IEEE International Conference on Robotics and Automation [C], 2003: 2836-2841
[31] Thrun S., Fox D., Burgard W.et al. Robust Monte Carlo localization for mobile robots [J]. Artificial Intelligence, 2001, 128(1-2): 99-141
[32]郭剑辉,赵春霞.一种新的粒子滤波SLAM算法[J].计算机研究与发展, 2008, 45(005): 853-860
[33] Dissanayake M., Newman P., Clark S.et al. A solution to the simultaneous localization and map building (SLAM) problem [J]. IEEE Transactions on Robotics and Automation, 2001, 17(3): 229-241
[34]武二永.基于视觉的机器人同时定位与地图构建[D].浙江大学, 2007
[35]许俊勇.移动机器人全景vSLAM研究[D].上海交通大学, 2008
[36]王海军.未知环境下移动机器人即时定位与地图创建[D].上海大学, 2009
[37] Harris C., Stephens M. A combined corner and edge detector [A]. Alvey vision conference [C], 1988: 50
[38] Shi J., Tomasi C. Good features to track [A]. 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994. Proceedings CVPR'94. [C], 1994: 593-600
[39] Lowe D.G. Distinctive image features from scale-invariant keypoints [J]. International journal of computer vision, 2004, 60(2): 91-110
[40] Lindeberg T. Scale-space theory in computer vision [M]. Springer, 1993
[41] Koenderink J.J. The structure of images [J]. Biological cybernetics, 1984, 50(5): 363-370
[42] Friedman J.H., Bentley J.L., Finkel R.A. An algorithm for finding best matches inlogarithmic expected time [J]. ACM Transactions on Mathematical Software (TOMS), 1977, 3(3): 209-226
[43] Arya S., Mount D.M., Netanyahu N.S.et al. An optimal algorithm for approximate nearest neighbor searching fixed dimensions [J]. Journal of the ACM (JACM), 1998, 45(6): 891-923
[44] Bay H., Tuytelaars T., Van Gool L. Surf: Speeded up robust features [J]. Computer Vision–ECCV 2006, 2006: 404-417
[45]孙传昱.基于前向视觉的机器人自定位及环境认知[D].大连理工大学, 2008
[46] Lindeberg T. Feature detection with automatic scale selection [J]. International Journal of Computer Vision, 1998, 30(2): 79-116
[47] Mikolajczyk K., Schmid C. Indexing based on scale invariant interest points [A]. Proc. ICCV [C], 2001: 525-531
[48] Mozosó., Gil A., Ballesta M.et al. Interest point detectors for visual slam [J]. Current Topics in Artificial Intelligence, 2007: 170-179
[49] Gil A., Mozos O.M., Ballesta M.et al. A comparative evaluation of interest point detectors and local descriptors for visual slam [J]. Machine Vision and Applications, 2009: 1-16
[50] Murillo A.C., Guerrero J.J., Sagues C. Surf features for efficient robot localization with omnidirectional images [A]. 2007 IEEE International Conference on Robotics and Automation [C], 2007: 3901-3907
[51]陈卫东,张飞.移动机器人的同步自定位与地图创建研究进展[J].控制理论与应用, 2005, 22(003): 455-460
[52]汤晓.基于激光测距仪的移动机器人同时定位与地图创建[D].山东大学, 2007