SLAM激光点云整体精配准位姿图技术
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摘要
基于同步定位与制图(simultaneous localization and mapping,SLAM)技术的激光扫描系统具有成本低、效率高的优点,近年来在测绘领域得到了广泛关注。虽然基于SLAM技术的激光扫描系统能够实现实时数据获取,但该数据获取方式难以保证点云精度,不同位置获取的同一地物的点云存在位置不一致。为了提高该类系统所获点云精度,本文提出一种分层次点云全局优化方法。该方法首先通过"点-切平面"迭代最近邻算法对重叠点云进行配准,形成扫描系统轨迹间的约束;然后构建位姿图对轨迹进行优化,利用优化后的轨迹对点云进行修正。算法通过将优化过程分解为局部和整体两个层次以提高计算效率。试验结果表明,优化后点云同名点对间的距离中误差减小约50%,内部不一致现象得到有效消除。
The laser scanning system based on simultaneous localization and mapping(SLAM) technology has the advantages of low cost and high efficiency. It has drawn wide attention in the field of surveying and mapping in recent years.Although real-time data acquisition can be achieved using SLAM technology, the precision of the data can't be ensured, and inconsistency exists in the acquired point cloud. In order to improve the precision of the point cloud obtained by this kind of system,this paper presents a hierarchical point cloud global optimization algorithm. Firstly, the "point-to-plane" Iterative closest point algorithm(ICP) is used to match the overlapping point clouds to form constraints between the trajectories of the scanning system.Then a pose graph is constructed to optimize the trajectory. Finally,the optimized trajectory is used to refine the point cloud. The computational efficiency is improved by decomposing the optimization process into two levels, i.e. local level and global level. The experimental results show that the RMSE of the distance between the corresponding points in overlapping areas is reduced by about 50% after optimization, and the internal inconsistency is effectively eliminated.
The laser scanning system based on simultaneous localization and mapping(SLAM) technology has the advantages of low cost and high efficiency. It has drawn wide attention in the field of surveying and mapping in recent years.Although real-time data acquisition can be achieved using SLAM technology, the precision of the data can't be ensured, and inconsistency exists in the acquired point cloud. In order to improve the precision of the point cloud obtained by this kind of system,this paper presents a hierarchical point cloud global optimization algorithm. Firstly, the "point-to-plane" Iterative closest point algorithm(ICP) is used to match the overlapping point clouds to form constraints between the trajectories of the scanning system.Then a pose graph is constructed to optimize the trajectory. Finally,the optimized trajectory is used to refine the point cloud. The computational efficiency is improved by decomposing the optimization process into two levels, i.e. local level and global level. The experimental results show that the RMSE of the distance between the corresponding points in overlapping areas is reduced by about 50% after optimization, and the internal inconsistency is effectively eliminated.
引文
[1] 梅文胜, 周燕芳, 周俊. 基于地面三维激光扫描的精细地形测绘[J]. 测绘通报, 2010(1): 53-56. MEI Wensheng, ZHOU Yanfang, ZHOU Jun. Fine topographic mapping based on ground three-dimensional laser scanning[J]. Bulletin of Surveying and Mapping, 2010(1): 53-56.
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[4] WANG Heng, WANG Bin, LIU Bingbing, et al. Pedestrian recognition and tracking using 3D LiDAR for autonomous vehicle[J]. Robotics and Autonomous Systems, 2017, 88: 71-78.
[5] 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 Mingjie, MIN Huaqing, LUO Ronghua. Graph-based SLAM: a survey[J]. Robot, 2013, 35(4): 500-512.
[6] LAUTERBACH H, BORRMANN D, HEβ R, et al. Evaluation of a backpack-mounted 3D mobile scanning system[J]. Remote Sensing, 2015, 7(10): 13753-13781.
[7] BOSSE M, ZLOT R. Place recognition using keypoint voting in large 3D LiDAR datasets[C]//Proceedings of 2013 IEEE International Conference on Robotics and Automation. Karlsruhe, Germany: IEEE, 2013: 2677-2684.
[8] ZLOT R, BOSSE M. Efficient large-scale three-dimensional mobile mapping for underground mines[J]. Journal of Field Robotics, 2014, 31(5): 758-779.
[9] HESS W, KOHLER D, RAPP H, et al. Real-time loop closure in 2D LiDAR SLAM[C]//Proceedings of 2016 IEEE International Conference on Robotics and Automation. Stockholm, Sweden: IEEE, 2016.
[10] CAO Jun, ZENG Bi, LIU Jianqi, et al. A novel relocation method for simultaneous localization and mapping based on deep learning algorithm[J]. Computers & Electrical Engineering, 2017, 63: 79-90.
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[12] 姚吉利, 马宁, 贾象阳, 等. 光束法区域网平差的地面激光扫描多站点云自动定向方法[J]. 测绘学报, 2014, 43(7): 711-716, 723. DOI: 10.13485/j.cnki.11-2089.2014.0099.YAO Jili, MA Ning, JIA Xiangyang, et al. Auto-registration for terrestrial laser scanning multi-stations point clouds with bundle block adjustment method[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(7): 711-716, 723. DOI: 10.13485/j.cnki.11-2089.2014.0099.
[13] 姚吉利, 贾象阳, 马宁, 等. 地面激光扫描多站点云整体定向平差模型[J]. 测绘学报, 2014, 43(8): 835-841. DOI:10.13485/j.cnki.11-2089.2014.0123.YAO Jili, JIA Xiangyang, MA Ning, et al. Overall orientation adjustment model of terrestrial laser scanning multi-station point clouds[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(8): 835-841. DOI:10.13485/j.cnki.11-2089.2014.0123.
[14] 闫利, 谭骏祥, 杨容浩, 等. 一种闭合条件约束的全局最优多视点云配准方法[J]. 测绘学报, 2016, 45(4): 418-424. DOI:10.11947/j.AGCS.2016.20150018.YAN Li, TAN Junxiang, YANG Ronghao, et al. A method of globally optimal registration for multi-view point clouds constrained by closed-loop conditions[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(4): 418-424. DOI:10.11947/j.AGCS.2016.20150018.
[15] KELBE D, VAN AARDT J, ROMANCZYK P, et al. Marker-free registration of forest terrestrial laser scanner data pairs with embedded confidence metrics[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(7): 4314-4330.
[16] KELBE D, VAN AARDT J, ROMANCZYK P, et al. Multiview marker-free registration of forest terrestrial laser scanner data with embedded confidence metrics[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2): 729-741.
[17] 李彩林, 郭宝云, 季铮. 多视角三维激光点云全局优化整体配准算法[J]. 测绘学报, 2015, 44(2): 183-189. DOI:10.11947/j.AGCS.2015.20130737.LI Cailin, GUO Baoyun, JI Zheng. Global optimization and whole registration algorithm of multi-view 3D laser point cloud[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(2): 183-189. DOI:10.11947/j.AGCS.2015.20130737.
[18] GRISETTI G, KUMMERLE R, STACHNISS C, et al. A tutorial on graph-based slam[J]. IEEE Intelligent Transportation Systems Magazine, 2010, 2(4): 31-43.
[19] BORRMANN D, ELSEBERG J, LINGEMANN K, et al. Globally consistent 3D mapping with scan matching[J]. Robotics and Autonomous Systems, 2008, 56(2): 130-142.
[20] BESL P J, MCKAY N D. A method for registration of 3D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2): 239-256.
[21] KüMMERLE R, GRISETTI G, STRASDAT H, et al. G2O: a general framework for graph optimization[C]//Proceedings of 2011 IEEE International Conference on Robotics and Automation. [S.l.]: IEEE, 2011: 3607-3613.
[22] CADENA C, CARLONE L, CARRILLO H, et al. Past, present, and future of simultaneous localization and mapping: toward the robust-perception age[J]. IEEE Transactions on Robotics, 2016, 32(6): 1309-1332.
[23] SHEPARD D P, HUMPHREYS T E. High-precision globally-referenced position and attitude via a fusion of visual SLAM, carrier-phase-based GPS, and inertial measurements[C]//Proceedings of 2014 IEEE/ION Position, Location and Navigation Symposium. Monterey, CA, USA: IEEE, 2014: 1309-1328.
[24] MARQUARDT D W. An algorithm for least-squares estimation of nonlinear parameters[J]. Journal of the Society for Industrial and Applied Mathematics, 1963, 11(2): 431-441.
[25] RUSU R B, MARTON Z C, BLODOW N, et al. Towards 3D point cloud based object maps for household environments[J]. Robotics and Autonomous Systems, 2008, 56(11): 927-941.
[26] PAULY M, KEISER R, GROSS M. Multi-scale feature extraction on point-sampled surfaces[J]. Computer Graphics Forum, 2003, 22(3): 281-289.
[27] POMERLEAU F, COLAS F, SIEGWART R, et al. Comparing ICP variants on real-world data sets[J]. Autonomous Robots, 2013, 34(3): 133-148.
[28] PULLI K. Multiview registration for large data sets[C]//Proceedings of the Second International Conference on 3D Digital Imaging and Modeling. Ottawa: IEEE, 1999: 160-168.
[29] LOW K L. Linear least-squares optimization for point-to-plane ICP surface registration[R]. [S.l.]: University of North Carolina at Chapel Hill, 2004, 4(TR04).
[30] 王永波, 杨化超, 刘燕华, 等. 线状特征约束下基于四元数描述的LiDAR点云配准方法[J]. 武汉大学学报(信息科学版), 2013, 38(9): 1057-1062. WANG Yongbo, YANG Huachao, LIU Yanhua, et al. Linear-feature-constrained registration of LiDAR point cloud via quaternion[J]. Geomatics and Information Science of Wuhan University, 2013, 38(9): 1057-1062.
[2] 杨必胜, 董震, 魏征, 等. 从车载激光扫描数据中提取复杂建筑物立面的方法[J]. 测绘学报, 2013, 42(3): 411-417. YANG Bisheng, DONG Zhen, WEI Zheng, et al. Extracting complex building facades from mobile laser scanning data[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(3): 411-417.
[3] TANG Jian, CHEN Yuwei, NIU Xiaoji, et al. LiDAR scan matching aided inertial navigation system in GNSS-denied environments[J]. Sensors, 2015, 15(7): 16710-16728.
[4] WANG Heng, WANG Bin, LIU Bingbing, et al. Pedestrian recognition and tracking using 3D LiDAR for autonomous vehicle[J]. Robotics and Autonomous Systems, 2017, 88: 71-78.
[5] 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 Mingjie, MIN Huaqing, LUO Ronghua. Graph-based SLAM: a survey[J]. Robot, 2013, 35(4): 500-512.
[6] LAUTERBACH H, BORRMANN D, HEβ R, et al. Evaluation of a backpack-mounted 3D mobile scanning system[J]. Remote Sensing, 2015, 7(10): 13753-13781.
[7] BOSSE M, ZLOT R. Place recognition using keypoint voting in large 3D LiDAR datasets[C]//Proceedings of 2013 IEEE International Conference on Robotics and Automation. Karlsruhe, Germany: IEEE, 2013: 2677-2684.
[8] ZLOT R, BOSSE M. Efficient large-scale three-dimensional mobile mapping for underground mines[J]. Journal of Field Robotics, 2014, 31(5): 758-779.
[9] HESS W, KOHLER D, RAPP H, et al. Real-time loop closure in 2D LiDAR SLAM[C]//Proceedings of 2016 IEEE International Conference on Robotics and Automation. Stockholm, Sweden: IEEE, 2016.
[10] CAO Jun, ZENG Bi, LIU Jianqi, et al. A novel relocation method for simultaneous localization and mapping based on deep learning algorithm[J]. Computers & Electrical Engineering, 2017, 63: 79-90.
[11] ELSEBERG J, BORRMANN D, NüCHTER A. 6D of Semi-rigid slam for mobile scanning[C]//Proceedings of 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Vilamoura, Portugal: IEEE, 2012: 1865-1870.
[12] 姚吉利, 马宁, 贾象阳, 等. 光束法区域网平差的地面激光扫描多站点云自动定向方法[J]. 测绘学报, 2014, 43(7): 711-716, 723. DOI: 10.13485/j.cnki.11-2089.2014.0099.YAO Jili, MA Ning, JIA Xiangyang, et al. Auto-registration for terrestrial laser scanning multi-stations point clouds with bundle block adjustment method[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(7): 711-716, 723. DOI: 10.13485/j.cnki.11-2089.2014.0099.
[13] 姚吉利, 贾象阳, 马宁, 等. 地面激光扫描多站点云整体定向平差模型[J]. 测绘学报, 2014, 43(8): 835-841. DOI:10.13485/j.cnki.11-2089.2014.0123.YAO Jili, JIA Xiangyang, MA Ning, et al. Overall orientation adjustment model of terrestrial laser scanning multi-station point clouds[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(8): 835-841. DOI:10.13485/j.cnki.11-2089.2014.0123.
[14] 闫利, 谭骏祥, 杨容浩, 等. 一种闭合条件约束的全局最优多视点云配准方法[J]. 测绘学报, 2016, 45(4): 418-424. DOI:10.11947/j.AGCS.2016.20150018.YAN Li, TAN Junxiang, YANG Ronghao, et al. A method of globally optimal registration for multi-view point clouds constrained by closed-loop conditions[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(4): 418-424. DOI:10.11947/j.AGCS.2016.20150018.
[15] KELBE D, VAN AARDT J, ROMANCZYK P, et al. Marker-free registration of forest terrestrial laser scanner data pairs with embedded confidence metrics[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(7): 4314-4330.
[16] KELBE D, VAN AARDT J, ROMANCZYK P, et al. Multiview marker-free registration of forest terrestrial laser scanner data with embedded confidence metrics[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2): 729-741.
[17] 李彩林, 郭宝云, 季铮. 多视角三维激光点云全局优化整体配准算法[J]. 测绘学报, 2015, 44(2): 183-189. DOI:10.11947/j.AGCS.2015.20130737.LI Cailin, GUO Baoyun, JI Zheng. Global optimization and whole registration algorithm of multi-view 3D laser point cloud[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(2): 183-189. DOI:10.11947/j.AGCS.2015.20130737.
[18] GRISETTI G, KUMMERLE R, STACHNISS C, et al. A tutorial on graph-based slam[J]. IEEE Intelligent Transportation Systems Magazine, 2010, 2(4): 31-43.
[19] BORRMANN D, ELSEBERG J, LINGEMANN K, et al. Globally consistent 3D mapping with scan matching[J]. Robotics and Autonomous Systems, 2008, 56(2): 130-142.
[20] BESL P J, MCKAY N D. A method for registration of 3D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2): 239-256.
[21] KüMMERLE R, GRISETTI G, STRASDAT H, et al. G2O: a general framework for graph optimization[C]//Proceedings of 2011 IEEE International Conference on Robotics and Automation. [S.l.]: IEEE, 2011: 3607-3613.
[22] CADENA C, CARLONE L, CARRILLO H, et al. Past, present, and future of simultaneous localization and mapping: toward the robust-perception age[J]. IEEE Transactions on Robotics, 2016, 32(6): 1309-1332.
[23] SHEPARD D P, HUMPHREYS T E. High-precision globally-referenced position and attitude via a fusion of visual SLAM, carrier-phase-based GPS, and inertial measurements[C]//Proceedings of 2014 IEEE/ION Position, Location and Navigation Symposium. Monterey, CA, USA: IEEE, 2014: 1309-1328.
[24] MARQUARDT D W. An algorithm for least-squares estimation of nonlinear parameters[J]. Journal of the Society for Industrial and Applied Mathematics, 1963, 11(2): 431-441.
[25] RUSU R B, MARTON Z C, BLODOW N, et al. Towards 3D point cloud based object maps for household environments[J]. Robotics and Autonomous Systems, 2008, 56(11): 927-941.
[26] PAULY M, KEISER R, GROSS M. Multi-scale feature extraction on point-sampled surfaces[J]. Computer Graphics Forum, 2003, 22(3): 281-289.
[27] POMERLEAU F, COLAS F, SIEGWART R, et al. Comparing ICP variants on real-world data sets[J]. Autonomous Robots, 2013, 34(3): 133-148.
[28] PULLI K. Multiview registration for large data sets[C]//Proceedings of the Second International Conference on 3D Digital Imaging and Modeling. Ottawa: IEEE, 1999: 160-168.
[29] LOW K L. Linear least-squares optimization for point-to-plane ICP surface registration[R]. [S.l.]: University of North Carolina at Chapel Hill, 2004, 4(TR04).
[30] 王永波, 杨化超, 刘燕华, 等. 线状特征约束下基于四元数描述的LiDAR点云配准方法[J]. 武汉大学学报(信息科学版), 2013, 38(9): 1057-1062. WANG Yongbo, YANG Huachao, LIU Yanhua, et al. Linear-feature-constrained registration of LiDAR point cloud via quaternion[J]. Geomatics and Information Science of Wuhan University, 2013, 38(9): 1057-1062.