ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Simultaneous localization and mapping based on RGB-D images with filter processing and pose optimization

  • Department of Automation, University of Science and Technology of China, Hefei 230027, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2017.08.005
  • Received Date: 01 September 2016
  • Rev Recd Date: 01 January 2017
  • Publish Date: 31 August 2017
    Abstract

  • Abstract

    RGB-D camera can capture color and depth images simultaneously, and is widely used for simultaneous localization and mapping (SLAM) research. In this article, The RGB-D SLAM method was improved from two aspects. Firstly, the point cloud filter method was improved to more effectively decrease the noise and redundancy of RGB-D camera data; secondly, an ICP algorithm was used to improve the estimated accuracy of the pose transformation matrix and the trajectories of camera movement. The proposed RGB-D SLAM method was verified on public datasets. The experimental results demonstrate that our RGB-D SLAM method can effectively improve the accuracy of the autonomous positioning and mapping of robots.

    Abstract

    RGB-D camera can capture color and depth images simultaneously, and is widely used for simultaneous localization and mapping (SLAM) research. In this article, The RGB-D SLAM method was improved from two aspects. Firstly, the point cloud filter method was improved to more effectively decrease the noise and redundancy of RGB-D camera data; secondly, an ICP algorithm was used to improve the estimated accuracy of the pose transformation matrix and the trajectories of camera movement. The proposed RGB-D SLAM method was verified on public datasets. The experimental results demonstrate that our RGB-D SLAM method can effectively improve the accuracy of the autonomous positioning and mapping of robots.
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    • References(36)
  • [1]
    李仁厚.自主移动机器人导论[M].西安: 西安交通大学出版社，2006.
    [2]
    SMITH R C, CHEESEMAN P. On the representation and estimation of spatial uncertainty[J]. International Journal of Robotics Research, 1986, 5(4): 56-58.
    [3]
    VAZQUEZ-MARTIN R, NUEZ P, DEL TORO J C, et al. Adaptive observation covariance for EKF-SLAM in indoor environments using laser data[C]// Proceedings of the IEEE Mediterranean Electrotechnical Conference. Malaga, Spain: IEEE Press, 2006: 445-448.
    [4]
    EL HAMZAOUI O, STEUX B. A fast scan matching for grid-based laser SLAM using streaming SIMD extensions[C]// Proceedings of the International Conference on Control Automation Robotics and Vision. Singapore: IEEE Press, 2010: 1986-1990.
    [5]
    周武, 赵春霞, 沈亚强, 等. 基于全局观测地图模型的SLAM研究[J]. 机器人, 2010, 32(5): 647-654.
    [6]
    REINA G, UNDERWOOD J, BROOKER G, et al. Radar-based perception for automous outdoor vehicle[J]. Journal of Field Robotics, 2011, 28(6): 894-913.
    [7]
    TOMONO M. Robust 3D SLAM with a stereo camera based on an edge-point ICP algorithm[C]// Proceedings of the IEEE International Conference on Robotics & Automation. Kobe, Japan: IEEE Press, 2009: 4306-4311.
    [8]
    陈伟, 吴涛, 李政, 等. 基于粒子滤波的单目视觉SLAM算法[J]. 机器人, 2008, 37(3): 242-253.
    [9]
    顾爽, 陈启军. 基于全景视觉匹配的移动机器人蒙特卡罗定位算法[J]. 控制理论与应用, 2012, 29(5): 585-591.
    [10]
    朱笑笑, 曹其新, 杨扬, 等. 基于RGB-D传感器的3D室内环境地图的实时创建[J]. 计算机工程与设计, 2014, 35(1): 203-207.
    [11]
    DRYANOVSKI I, VALENTI R G, XIAO J Z. Fast visual odometry and mapping from RGB-D data[C]// Proceedings of the IEEE International Conference on Robotics and Automation. Pisataway, USA: IEEE Press, 2013: 2305-2310.
    [12]
    HENRY P, KRAININ M, HERBST E, et al. RGB-D mapping: Using depth cameras for dense 3D modeling of indoor environments[C]//Proceedings of the 12th International Symposium on Experimental Robotics. Delhi, India: CiteSeer, 2010.
    [13]
    HENRY P, KRAININ M, HERBST E, et al. RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments[J]. International Journal of Robotics Research, 2012, 31(5): 647-663.
    [14]
    欧美英, 孙海滨, 李世华. 具有外部扰动的非完整移动机器人的有限时间分钟控制[J]. 中国科学技术大学学报, 2012, 42(5): 405-414.
    OU Meiying, SUN Haibin, LI Shihua. Finite time tracing control of a nonholonomic mobile robot with external disturbance[J]. Journal of University of Science and Technology of China, 2012, 42(5): 405-414.
    [15]
    吕妍, 陈宗海. 不确定环境信息下基于方位关系的路径规划算法[J]. 中国科学技术大学学报, 2013, 43(10): 782-789, 829.
    LV Yan, CHEN Zonghai. Path planning algorithm based on directional relationship with uncertain environment information[J].Journal of University of Science and Technology of China, 2013, 43(10): 782-789, 829.
    [16]
    谷丰, 王争, 宋琦, 等. 空地机器人协作导航方法与实验研究[J]. 中国科学技术大学学报, 2012, 42(5): 398-404.
    GU Feng, WANG Zheng, SONG Qi, et al. Theoretical and experimental study of air-ground fluidized bed[J].Journal of University of Science and Technology of China, 2012, 42(5): 398-404.
    [17]
    ENDRES F, HESS J, ENGELHARD N, et al. An evaluation of the RGB-D SLAM system[C]// Proceedings of the IEEE International Conference on Robotics and Automation. Minnesota, USA: IEEE Press, 2012: 1691-1696.
    [18]
    ENDRES F, HESS J, ENGELHARD N, et al. 3-D mapping with an RGB-D camera[J]. IEEE Transactions on Robotics, 2014, 30(1): 177-187.
    [19]
    FISCHLER M A, BOLLES R C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the Association for Computing Machinery, 1981, 24(6): 381-395.
    [20]
    BESL P J, MCKAY N D. Method for registration of 3-D shapes[C]// Proceedings of the International Society for Optics and Photonics. 1992: 586-606.
    [21]
    STURM J, ENGELHARD N, ENDRES F, et al. A benchmark for the evaluation of RGB-D SLAM systems[C]// Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura, Portugal: IEEE Press, 2012: 573-580.
    [22]
    Computer Vision Group. RGB-D SLAM Dataset and benchmark[EB/OL]. http://vision.in.tum.de/data/datasets/rgbd-dataset.
    [23]
    KMMERLE R, GRISETTI G, STRASDAT H, et al. g2o: A general framework for graph optimization[C]// Proceedings of the IEEE International Conference on Robotics and Automation. Shanghai, China: IEEE Press, 2011: 3607-3613.
    [24]
    WURM K M, HORNUNG A, BENNEWITZ M, et al. OctoMap: A probabilistic, flexible, and compact 3D map representation for robotic systems[C]// Proceedings of the International Conference on Robotics and Automation 2010 Workshop on Best Practice in 3D Perception and Modeling for Mobile Manipulation. 2010, 16(3): 403-412.
    [25]
    BRADSKI G, KAEHLER A. Learning OpenCV: Computer Vision with the OpenCV Library[M]. O'Reilly Media, Inc. 2008.
    [26]
    LOWE D G. Object recognition from local scale-invariant features[C]// Proceedings of the IEEE International Conference on Computer Vision. Kerkyra, Greece: IEEE Press, 1999: 1150-1157.
    [27]
    RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB: An efficient alternative to SIFT or SURF[C]// Proceedings of the IEEE International Conference on Computer Vision. Barcelona, Spain: IEEE Press, 2011: 2564-2571.
    [28]
    冯亦东, 孙跃. 基于SURF特征提取和FLANN搜索的图像匹配算法[J]. 图学学报, 2015, 36(4): 650-654.
    [29]
    BAY H, TUYTELAARS T, VAN GOOL L. SURF: Speeded up robust features[C]// Proceedings of European Conference on Computer Vision. Graz, Austria: Springer, 2006: 404-407.
    [30]
    刘志斌, 吴显亮, 徐文立, 等. 视觉SLAM中的基于误匹配风险预测的特征选择[J]. 机器人, 2010, 32(5): 635-641.
    [31]
    RUSU R B. Semantic 3D object maps for everyday manipulation in human living environments[J]. KI-Künstliche Intelligenz, 2010, 24(4): 345-348.
    [32]
    王亚军, 蔺启忠, 王钦军, 等. 应用区域光谱库及分段滤波方法改进矿物识别精度的研究[J]. 光谱学与光谱分析, 2012, 32(8): 2065-2069.
    [33]
    QUANG H P, QUOC N L. Some improvements in the RGB-D SLAM system[C]// Proceedings of the IEEE RVIF International Conference on Computing & Communication Technologies-Research, Innovation, and Vision for the Future. Can Tho, Vietnam: IEEE Press, 2015: 112-116.
    [34]
    SNDERHAUF N, PROTZEL P. Towards a robust back-end for pose graph SLAM[C]// Proceedings of the IEEE International Conference on Robotics and Automation. Saint Paul, Brazil: IEEE Press, 2012, 24(7): 1254-1261.
    [35]
    BRUGSLI D, BROENINK J F, KROEGER T. et al. Simulation, Modeling, and Programming for Autonomous Robots[M]. Berlin: Springer, 2008.
    [36]
    ZHANG L, SHEN P, DING J, et al. An improved RGB-D SLAM algorithm based on Kinect sensor[C]// Proceedings of the IEEE International Conference on Advanced Intelligent Mechatronics. Busan, Korea: IEEE Press, 2015: 555-562.
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    [1]
    李仁厚.自主移动机器人导论[M].西安: 西安交通大学出版社，2006.
    [2]
    SMITH R C, CHEESEMAN P. On the representation and estimation of spatial uncertainty[J]. International Journal of Robotics Research, 1986, 5(4): 56-58.
    [3]
    VAZQUEZ-MARTIN R, NUEZ P, DEL TORO J C, et al. Adaptive observation covariance for EKF-SLAM in indoor environments using laser data[C]// Proceedings of the IEEE Mediterranean Electrotechnical Conference. Malaga, Spain: IEEE Press, 2006: 445-448.
    [4]
    EL HAMZAOUI O, STEUX B. A fast scan matching for grid-based laser SLAM using streaming SIMD extensions[C]// Proceedings of the International Conference on Control Automation Robotics and Vision. Singapore: IEEE Press, 2010: 1986-1990.
    [5]
    周武, 赵春霞, 沈亚强, 等. 基于全局观测地图模型的SLAM研究[J]. 机器人, 2010, 32(5): 647-654.
    [6]
    REINA G, UNDERWOOD J, BROOKER G, et al. Radar-based perception for automous outdoor vehicle[J]. Journal of Field Robotics, 2011, 28(6): 894-913.
    [7]
    TOMONO M. Robust 3D SLAM with a stereo camera based on an edge-point ICP algorithm[C]// Proceedings of the IEEE International Conference on Robotics & Automation. Kobe, Japan: IEEE Press, 2009: 4306-4311.
    [8]
    陈伟, 吴涛, 李政, 等. 基于粒子滤波的单目视觉SLAM算法[J]. 机器人, 2008, 37(3): 242-253.
    [9]
    顾爽, 陈启军. 基于全景视觉匹配的移动机器人蒙特卡罗定位算法[J]. 控制理论与应用, 2012, 29(5): 585-591.
    [10]
    朱笑笑, 曹其新, 杨扬, 等. 基于RGB-D传感器的3D室内环境地图的实时创建[J]. 计算机工程与设计, 2014, 35(1): 203-207.
    [11]
    DRYANOVSKI I, VALENTI R G, XIAO J Z. Fast visual odometry and mapping from RGB-D data[C]// Proceedings of the IEEE International Conference on Robotics and Automation. Pisataway, USA: IEEE Press, 2013: 2305-2310.
    [12]
    HENRY P, KRAININ M, HERBST E, et al. RGB-D mapping: Using depth cameras for dense 3D modeling of indoor environments[C]//Proceedings of the 12th International Symposium on Experimental Robotics. Delhi, India: CiteSeer, 2010.
    [13]
    HENRY P, KRAININ M, HERBST E, et al. RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments[J]. International Journal of Robotics Research, 2012, 31(5): 647-663.
    [14]
    欧美英, 孙海滨, 李世华. 具有外部扰动的非完整移动机器人的有限时间分钟控制[J]. 中国科学技术大学学报, 2012, 42(5): 405-414.
    OU Meiying, SUN Haibin, LI Shihua. Finite time tracing control of a nonholonomic mobile robot with external disturbance[J]. Journal of University of Science and Technology of China, 2012, 42(5): 405-414.
    [15]
    吕妍, 陈宗海. 不确定环境信息下基于方位关系的路径规划算法[J]. 中国科学技术大学学报, 2013, 43(10): 782-789, 829.
    LV Yan, CHEN Zonghai. Path planning algorithm based on directional relationship with uncertain environment information[J].Journal of University of Science and Technology of China, 2013, 43(10): 782-789, 829.
    [16]
    谷丰, 王争, 宋琦, 等. 空地机器人协作导航方法与实验研究[J]. 中国科学技术大学学报, 2012, 42(5): 398-404.
    GU Feng, WANG Zheng, SONG Qi, et al. Theoretical and experimental study of air-ground fluidized bed[J].Journal of University of Science and Technology of China, 2012, 42(5): 398-404.
    [17]
    ENDRES F, HESS J, ENGELHARD N, et al. An evaluation of the RGB-D SLAM system[C]// Proceedings of the IEEE International Conference on Robotics and Automation. Minnesota, USA: IEEE Press, 2012: 1691-1696.
    [18]
    ENDRES F, HESS J, ENGELHARD N, et al. 3-D mapping with an RGB-D camera[J]. IEEE Transactions on Robotics, 2014, 30(1): 177-187.
    [19]
    FISCHLER M A, BOLLES R C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the Association for Computing Machinery, 1981, 24(6): 381-395.
    [20]
    BESL P J, MCKAY N D. Method for registration of 3-D shapes[C]// Proceedings of the International Society for Optics and Photonics. 1992: 586-606.
    [21]
    STURM J, ENGELHARD N, ENDRES F, et al. A benchmark for the evaluation of RGB-D SLAM systems[C]// Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura, Portugal: IEEE Press, 2012: 573-580.
    [22]
    Computer Vision Group. RGB-D SLAM Dataset and benchmark[EB/OL]. http://vision.in.tum.de/data/datasets/rgbd-dataset.
    [23]
    KMMERLE R, GRISETTI G, STRASDAT H, et al. g2o: A general framework for graph optimization[C]// Proceedings of the IEEE International Conference on Robotics and Automation. Shanghai, China: IEEE Press, 2011: 3607-3613.
    [24]
    WURM K M, HORNUNG A, BENNEWITZ M, et al. OctoMap: A probabilistic, flexible, and compact 3D map representation for robotic systems[C]// Proceedings of the International Conference on Robotics and Automation 2010 Workshop on Best Practice in 3D Perception and Modeling for Mobile Manipulation. 2010, 16(3): 403-412.
    [25]
    BRADSKI G, KAEHLER A. Learning OpenCV: Computer Vision with the OpenCV Library[M]. O'Reilly Media, Inc. 2008.
    [26]
    LOWE D G. Object recognition from local scale-invariant features[C]// Proceedings of the IEEE International Conference on Computer Vision. Kerkyra, Greece: IEEE Press, 1999: 1150-1157.
    [27]
    RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB: An efficient alternative to SIFT or SURF[C]// Proceedings of the IEEE International Conference on Computer Vision. Barcelona, Spain: IEEE Press, 2011: 2564-2571.
    [28]
    冯亦东, 孙跃. 基于SURF特征提取和FLANN搜索的图像匹配算法[J]. 图学学报, 2015, 36(4): 650-654.
    [29]
    BAY H, TUYTELAARS T, VAN GOOL L. SURF: Speeded up robust features[C]// Proceedings of European Conference on Computer Vision. Graz, Austria: Springer, 2006: 404-407.
    [30]
    刘志斌, 吴显亮, 徐文立, 等. 视觉SLAM中的基于误匹配风险预测的特征选择[J]. 机器人, 2010, 32(5): 635-641.
    [31]
    RUSU R B. Semantic 3D object maps for everyday manipulation in human living environments[J]. KI-Künstliche Intelligenz, 2010, 24(4): 345-348.
    [32]
    王亚军, 蔺启忠, 王钦军, 等. 应用区域光谱库及分段滤波方法改进矿物识别精度的研究[J]. 光谱学与光谱分析, 2012, 32(8): 2065-2069.
    [33]
    QUANG H P, QUOC N L. Some improvements in the RGB-D SLAM system[C]// Proceedings of the IEEE RVIF International Conference on Computing & Communication Technologies-Research, Innovation, and Vision for the Future. Can Tho, Vietnam: IEEE Press, 2015: 112-116.
    [34]
    SNDERHAUF N, PROTZEL P. Towards a robust back-end for pose graph SLAM[C]// Proceedings of the IEEE International Conference on Robotics and Automation. Saint Paul, Brazil: IEEE Press, 2012, 24(7): 1254-1261.
    [35]
    BRUGSLI D, BROENINK J F, KROEGER T. et al. Simulation, Modeling, and Programming for Autonomous Robots[M]. Berlin: Springer, 2008.
    [36]
    ZHANG L, SHEN P, DING J, et al. An improved RGB-D SLAM algorithm based on Kinect sensor[C]// Proceedings of the IEEE International Conference on Advanced Intelligent Mechatronics. Busan, Korea: IEEE Press, 2015: 555-562.
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