同步定位和地图构建(simultaneous localization and mapping,SLAM)是移动机器人在未知环境中完成地图构建和定位任务的关键技术。针对多机器人SLAM中的地图融合问题,提出一种启发式的搜索方法引导局部地图的重复区域进行地图融合。每...同步定位和地图构建(simultaneous localization and mapping,SLAM)是移动机器人在未知环境中完成地图构建和定位任务的关键技术。针对多机器人SLAM中的地图融合问题,提出一种启发式的搜索方法引导局部地图的重复区域进行地图融合。每个机器人可以在不了解其相对位置的情况下建立局部地图,并将局部地图信息发送至同一工作站中,以局部地图的相似性为判断指标融合得到最优的全局地图。在机器人实物平台上进行验证,结果证明了多机器人SLAM的地图融合算法的有效性和准确性。展开更多
为解决传统机器人SLAM在复杂场景下难以保持实时性和稳定性的问题,提出一种对ORB(oriented FAST and rotated BREIEF)特征点过于密集的改进方法,对前端视觉里程计以及SLAM后端进行优化。前端利用划分像素结合四叉树完成特征提取,对像素...为解决传统机器人SLAM在复杂场景下难以保持实时性和稳定性的问题,提出一种对ORB(oriented FAST and rotated BREIEF)特征点过于密集的改进方法,对前端视觉里程计以及SLAM后端进行优化。前端利用划分像素结合四叉树完成特征提取,对像素进行划分提高部分区域提取到特征点的概率,四叉树方法对特征点进行均匀分配提取。RANSAC组合EPNP+ICP的方式减少求解相机运动的误差,基于词袋模型和G2O进行闭环检测和图优化,生成轨迹一致的全局点云地图。将传统的算法和改进的算法在TUM数据集下进行实验对比,实验结果表明,改进后算法的实时性和稳定性明显提高。展开更多
为了解决单个移动机器人在未知环境下,构建地图不精确及无法实现并行任务的问题,提出了基于双目视觉的多机器人SLAM (Simultaneous Localization and Mapping),此方法可以大幅度提高机器人的定位精度和构图的准确性。以双机器人为例,提...为了解决单个移动机器人在未知环境下,构建地图不精确及无法实现并行任务的问题,提出了基于双目视觉的多机器人SLAM (Simultaneous Localization and Mapping),此方法可以大幅度提高机器人的定位精度和构图的准确性。以双机器人为例,提出了协作SLAM的实现方法,通过ORB-SLAM2算法,将两个机器人获得的关键帧进行共享,通过相对位姿估计不断地更新局部地图,然后将局部地图进行融合,从而实现多机器人SLAM。展开更多
[目的/意义]在大田作业、野外放牧、果园采收等典型农业应用场景下,多机器人(包括移动式智能农机装备等)高精度快速协同同步定位与建图(Simultaneous Localization and Mapping,SLAM)是智慧农业乃至无人农场的关键基础和核心支撑。与单...[目的/意义]在大田作业、野外放牧、果园采收等典型农业应用场景下,多机器人(包括移动式智能农机装备等)高精度快速协同同步定位与建图(Simultaneous Localization and Mapping,SLAM)是智慧农业乃至无人农场的关键基础和核心支撑。与单机器人SLAM相比,多机器人协同SLAM具有精度高、范围广、实时性强、扩展性好等优势,但在农业种植和养殖等自然复杂环境下,由于场景动态可变、地形复杂多变、环境丰富多样、通信约束受限等多重因素叠加影响,尚存在诸多问题与挑战。[进展]现有研究主要是从通用基础技术的视角对多机器人SLAM的研究脉络、优缺点、适用条件和关键核心问题等方面进行总结归纳,但缺乏针对农业复杂场景特性的剖析。本研究面向农业复杂场景的主要特征,以“多传感器数据融合—协同定位—协同建图—回环检测”为关键技术主线,分析了多机器人协同SLAM的优缺点及其在农业领域的适用性;从多机器人协同作业的视角,明晰了集中式、分布式和混合式三种主要协同框架的优势、局限性及适用的典型农业应用场景;进而探讨了农业复杂场景下多机器人SLAM存在的多传感器融合精度偏低、协同通信环境受限、相对位姿估计准确性不高等突出问题。[结论/展望]从优化数据融合底层算法、融合深度学习和强化学习、引入大语言模型、应用数字孪生技术等方面,对农业复杂环境下多机器人SLAM的未来发展方向和趋势进行了展望。展开更多
移动机器人同步定位与建图问题(Simultaneous localization and mapping,SLAM)是机器人能否在未知环境中实现完全自主的关键问题之一.其中,机器人定位估计对于保持地图的一致性非常重要.本文分析了SLAM问题中机器人定位误差的收敛特性....移动机器人同步定位与建图问题(Simultaneous localization and mapping,SLAM)是机器人能否在未知环境中实现完全自主的关键问题之一.其中,机器人定位估计对于保持地图的一致性非常重要.本文分析了SLAM问题中机器人定位误差的收敛特性.分析表明随着机器人的运动,机器人定位误差总体上逐渐增大;在完全未知环境中无法预测机器人定位误差的上限.根据理论分析,本文提出了一种控制机器人定位误差在单位距离上增长速度的算法.该算法通过搜索获得满足定位误差限制的最佳的机器人运动速度,从而控制机器人定位误差的增长.展开更多
针对光线强度对机器人视觉同步定位与地图构建(Simultaneous Localization and Mapping,SLAM)建图信息量、时效性和鲁棒性影响大的问题,提出一种基于激光雷达(Light Detection And Ranging,LiDAR)增强的视觉SLAM多机器人协作地图构建方...针对光线强度对机器人视觉同步定位与地图构建(Simultaneous Localization and Mapping,SLAM)建图信息量、时效性和鲁棒性影响大的问题,提出一种基于激光雷达(Light Detection And Ranging,LiDAR)增强的视觉SLAM多机器人协作地图构建方法。在地图构建过程中,将LiDAR深度测量值集成到现有的特征点检测和特征描述子同步定位与地图构建(Oriented FAST and Rotated BRIEF-Simultaneous Localization and Mapping,ORB-SLAM3)算法中,利用改进的扩展卡尔曼滤波算法将激光雷达的高精度数据和视觉传感器的时序信息融合在一起,获得单个机器人的位姿状态,结合深度图进行单个机器人稠密点云地图的构建;利用关键帧跟踪模型和迭代最近点(Iterative Closest Point,ICP)算法得到存在共识关系的机器人之间的坐标转换关系,进而得到各机器人的世界坐标系,在世界坐标系中实现多机器人协作地图的融合与构建。在Gazebo仿真平台中实验验证了方法的时效性和鲁棒性。展开更多
针对单机器人同时定位与地图构建(Simultaneous Localization And Mapping,SLAM)算法构图效率低的问题,设计了基于ORB-SLAM3的多机器人协作SLAM的实时融合方案。首先,向两个SLAM进程输入相同的关键帧并以此帧所在位姿为世界坐标系完成...针对单机器人同时定位与地图构建(Simultaneous Localization And Mapping,SLAM)算法构图效率低的问题,设计了基于ORB-SLAM3的多机器人协作SLAM的实时融合方案。首先,向两个SLAM进程输入相同的关键帧并以此帧所在位姿为世界坐标系完成初始化,然后各机器人以此坐标系为基础完成定位于建图,同时将地图汇总融合完成任务。此方案避开了传统地图融合问题中特征匹配与位姿估计的技术难点。在Gazebo仿真环境进行测试实验,验证了方案的有效性、实时性和鲁棒性。展开更多
An innovative multi-robot simultaneous localization and mapping(SLAM)is proposed based on a mobile Ad hoc local wireless sensor network(Ad-WSN).Multiple followed-robots equipped with the wireless link RS232/485module ...An innovative multi-robot simultaneous localization and mapping(SLAM)is proposed based on a mobile Ad hoc local wireless sensor network(Ad-WSN).Multiple followed-robots equipped with the wireless link RS232/485module act as mobile nodes,with various on-board sensors,Tp-link wireless local area network cards,and Tp-link wireless routers.The master robot with embedded industrial PC and a complete robot control system autonomously performs the SLAM task by exchanging information with multiple followed-robots by using this self-organizing mobile wireless network.The PC on the remote console can monitor multi-robot SLAM on-site and provide direct motion control of the robots.This mobile Ad-WSN complements an environment devoid of usual GPS signals for the robots performing SLAM task in search and rescue environments.In post-disaster areas,the network is usually absent or variable and the site scene is cluttered with obstacles.To adapt to such harsh situations,the proposed self-organizing mobile Ad-WSN enables robots to complete the SLAM process while improving the performances of object of interest identification and exploration area coverage.The information of localization and mapping can communicate freely among multiple robots and remote PC control center via this mobile Ad-WSN.Therefore,the autonomous master robot runs SLAM algorithms while exchanging information with multiple followed-robots and with the remote PC control center via this local WSN environment.Simulations and experiments validate the improved performances of the exploration area coverage,object marked,and loop closure,which are adapted to search and rescue post-disaster cluttered environments.展开更多
为提高基于激光雷达的同步定位和建图(simultaneous localization and mapping,SLAM)精度,提出一种基于因子图的高效率、高精度的激光雷达SLAM框架。采用一种基于滑动窗口的因子图方法,将当前帧进行帧间匹配得到相对位姿,按照一定规则...为提高基于激光雷达的同步定位和建图(simultaneous localization and mapping,SLAM)精度,提出一种基于因子图的高效率、高精度的激光雷达SLAM框架。采用一种基于滑动窗口的因子图方法,将当前帧进行帧间匹配得到相对位姿,按照一定规则选出关键帧,将关键帧与全局地图进行匹配得到绝对位姿;构建一个因子图,将得到的连续帧之间的相对位姿与关键帧的绝对位姿作为优化因子,机器人的位姿作为状态节点放入因子图中进行位姿优化,得到高频率的机器人位姿以及全局一致的环境地图。结果表明:该算法能够减小误差的累积,具有更高的定位精度。展开更多
The task of simultaneous localization and mapping （SLAM） is to build environmental map and locate the position of mobile robot at the same time. FastSLAM 2.0 is one of powerful techniques to solve the SLAM problem. ...The task of simultaneous localization and mapping （SLAM） is to build environmental map and locate the position of mobile robot at the same time. FastSLAM 2.0 is one of powerful techniques to solve the SLAM problem. However, there are two obvious limitations in FastSLAM 2.0, one is the linear approximations of nonlinear functions which would cause the filter inconsistent and the other is the ＂particle depletion＂ phenomenon. A kind of PSO ＆ Hjj-based FastSLAM 2.0 algorithm is proposed. For maintaining the estimation accuracy, H~ filter is used instead of EKF for overcoming the inaccuracy caused by the linear approximations of nonlinear functions. The unreasonable proposal distribution of particle greatly influences the pose state estimation of robot. A new sampling strategy based on PSO （particle swarm optimization） is presented to solve the ＂particle depletion＂ phenomenon and improve the accuracy of pose state estimation. The proposed approach overcomes the obvious drawbacks of standard FastSLAM 2.0 algorithm and enhances the robustness and efficiency in the parts of consistency of filter and accuracy of state estimation in SLAM. Simulation results demonstrate the superiority of the proposed approach.展开更多
A novel mobile robot simultaneous localization and mapping (SLAM) method is implemented by using the Rao- Blackwellized particle filter (RBPF) for monocular vision-based autonomous robot in unknown indoor environment....A novel mobile robot simultaneous localization and mapping (SLAM) method is implemented by using the Rao- Blackwellized particle filter (RBPF) for monocular vision-based autonomous robot in unknown indoor environment. The particle filter combined with unscented Kalman filter (UKF) for extending the path posterior by sampling new poses integrating the current observation. Landmark position estimation and update is implemented through UKF. Furthermore, the number of resampling steps is determined adaptively, which greatly reduces the particle depletion problem. Monocular CCD camera mounted on the robot tracks the 3D natural point landmarks structured with matching image feature pairs extracted through Scale Invariant Feature Transform (SIFT). The matching for multi-dimension SIFT features which are highly distinctive due to a special descriptor is implemented with a KD-Tree. Experiments on the robot Pioneer3 showed that our method is very precise and stable.展开更多
Simultaneous localization and mapping(SLAM)is a crucial technology for construction robots,enabling complex environment mapping and localization at construction sites and facilitating autonomous construction.However,c...Simultaneous localization and mapping(SLAM)is a crucial technology for construction robots,enabling complex environment mapping and localization at construction sites and facilitating autonomous construction.However,construction sites,particularly those of large public buildings,are characterized by complex spatial structures,timevarying conditions,and dynamic uncertainties.Achieving accurate SLAM in such environments is a highly challenging task for construction robots.In this paper,a SLAM dataset is created specifically for construction sites of large public buildings,and the performance of current mainstream open source 3D light detection and ranging(LiDAR)SLAM algorithms is evaluated.Firstly,an experimental platform for construction robots is established,and a SLAM dataset is generated by collecting data at the construction site of a large public building in Xi’an.Secondly,a simulation environment is developed based on the construction drawings of the ongoing project.A simulation model of the construction robot is created according to the experimental platform,and a SLAM dataset for the simulated construction site environment is compiled by data collection.Finally,comparative experiments involving ten types of open source 3D LiDAR SLAM algorithms are conducted,and the accuracy of SLAM pose estimation and point cloud maps is assessed.The experimental results offer valuable references for SLAM algorithm research for construction robots in construction site environments.Specifically,they reveal the strengths and limitations of existing algorithms under construction-specific challenges,guiding future algorithm optimization.This work not only bridges the gap in construction-oriented SLAM dataset resources but also promotes the practical application of autonomous construction robots in large public building projects.展开更多
文摘同步定位和地图构建(simultaneous localization and mapping,SLAM)是移动机器人在未知环境中完成地图构建和定位任务的关键技术。针对多机器人SLAM中的地图融合问题,提出一种启发式的搜索方法引导局部地图的重复区域进行地图融合。每个机器人可以在不了解其相对位置的情况下建立局部地图,并将局部地图信息发送至同一工作站中,以局部地图的相似性为判断指标融合得到最优的全局地图。在机器人实物平台上进行验证,结果证明了多机器人SLAM的地图融合算法的有效性和准确性。
文摘为解决传统机器人SLAM在复杂场景下难以保持实时性和稳定性的问题,提出一种对ORB(oriented FAST and rotated BREIEF)特征点过于密集的改进方法,对前端视觉里程计以及SLAM后端进行优化。前端利用划分像素结合四叉树完成特征提取,对像素进行划分提高部分区域提取到特征点的概率,四叉树方法对特征点进行均匀分配提取。RANSAC组合EPNP+ICP的方式减少求解相机运动的误差,基于词袋模型和G2O进行闭环检测和图优化,生成轨迹一致的全局点云地图。将传统的算法和改进的算法在TUM数据集下进行实验对比,实验结果表明,改进后算法的实时性和稳定性明显提高。
文摘为了解决单个移动机器人在未知环境下,构建地图不精确及无法实现并行任务的问题,提出了基于双目视觉的多机器人SLAM (Simultaneous Localization and Mapping),此方法可以大幅度提高机器人的定位精度和构图的准确性。以双机器人为例,提出了协作SLAM的实现方法,通过ORB-SLAM2算法,将两个机器人获得的关键帧进行共享,通过相对位姿估计不断地更新局部地图,然后将局部地图进行融合,从而实现多机器人SLAM。
文摘[目的/意义]在大田作业、野外放牧、果园采收等典型农业应用场景下,多机器人(包括移动式智能农机装备等)高精度快速协同同步定位与建图(Simultaneous Localization and Mapping,SLAM)是智慧农业乃至无人农场的关键基础和核心支撑。与单机器人SLAM相比,多机器人协同SLAM具有精度高、范围广、实时性强、扩展性好等优势,但在农业种植和养殖等自然复杂环境下,由于场景动态可变、地形复杂多变、环境丰富多样、通信约束受限等多重因素叠加影响,尚存在诸多问题与挑战。[进展]现有研究主要是从通用基础技术的视角对多机器人SLAM的研究脉络、优缺点、适用条件和关键核心问题等方面进行总结归纳,但缺乏针对农业复杂场景特性的剖析。本研究面向农业复杂场景的主要特征,以“多传感器数据融合—协同定位—协同建图—回环检测”为关键技术主线,分析了多机器人协同SLAM的优缺点及其在农业领域的适用性;从多机器人协同作业的视角,明晰了集中式、分布式和混合式三种主要协同框架的优势、局限性及适用的典型农业应用场景;进而探讨了农业复杂场景下多机器人SLAM存在的多传感器融合精度偏低、协同通信环境受限、相对位姿估计准确性不高等突出问题。[结论/展望]从优化数据融合底层算法、融合深度学习和强化学习、引入大语言模型、应用数字孪生技术等方面,对农业复杂环境下多机器人SLAM的未来发展方向和趋势进行了展望。
文摘移动机器人同步定位与建图问题(Simultaneous localization and mapping,SLAM)是机器人能否在未知环境中实现完全自主的关键问题之一.其中,机器人定位估计对于保持地图的一致性非常重要.本文分析了SLAM问题中机器人定位误差的收敛特性.分析表明随着机器人的运动,机器人定位误差总体上逐渐增大;在完全未知环境中无法预测机器人定位误差的上限.根据理论分析,本文提出了一种控制机器人定位误差在单位距离上增长速度的算法.该算法通过搜索获得满足定位误差限制的最佳的机器人运动速度,从而控制机器人定位误差的增长.
文摘针对光线强度对机器人视觉同步定位与地图构建(Simultaneous Localization and Mapping,SLAM)建图信息量、时效性和鲁棒性影响大的问题,提出一种基于激光雷达(Light Detection And Ranging,LiDAR)增强的视觉SLAM多机器人协作地图构建方法。在地图构建过程中,将LiDAR深度测量值集成到现有的特征点检测和特征描述子同步定位与地图构建(Oriented FAST and Rotated BRIEF-Simultaneous Localization and Mapping,ORB-SLAM3)算法中,利用改进的扩展卡尔曼滤波算法将激光雷达的高精度数据和视觉传感器的时序信息融合在一起,获得单个机器人的位姿状态,结合深度图进行单个机器人稠密点云地图的构建;利用关键帧跟踪模型和迭代最近点(Iterative Closest Point,ICP)算法得到存在共识关系的机器人之间的坐标转换关系,进而得到各机器人的世界坐标系,在世界坐标系中实现多机器人协作地图的融合与构建。在Gazebo仿真平台中实验验证了方法的时效性和鲁棒性。
文摘针对单机器人同时定位与地图构建(Simultaneous Localization And Mapping,SLAM)算法构图效率低的问题,设计了基于ORB-SLAM3的多机器人协作SLAM的实时融合方案。首先,向两个SLAM进程输入相同的关键帧并以此帧所在位姿为世界坐标系完成初始化,然后各机器人以此坐标系为基础完成定位于建图,同时将地图汇总融合完成任务。此方案避开了传统地图融合问题中特征匹配与位姿估计的技术难点。在Gazebo仿真环境进行测试实验,验证了方案的有效性、实时性和鲁棒性。
基金Projects(61573213,61473174,61473179)supported by the National Natural Science Foundation of ChinaProjects(ZR2015PF009,ZR2014FM007)supported by the Natural Science Foundation of Shandong Province,China+1 种基金Project(2014GGX103038)supported by the Shandong Province Science and Technology Development Program,ChinaProject(2014ZZCX04302)supported by the Special Technological Program of Transformation of Initiatively Innovative Achievements in Shandong Province,China
文摘An innovative multi-robot simultaneous localization and mapping(SLAM)is proposed based on a mobile Ad hoc local wireless sensor network(Ad-WSN).Multiple followed-robots equipped with the wireless link RS232/485module act as mobile nodes,with various on-board sensors,Tp-link wireless local area network cards,and Tp-link wireless routers.The master robot with embedded industrial PC and a complete robot control system autonomously performs the SLAM task by exchanging information with multiple followed-robots by using this self-organizing mobile wireless network.The PC on the remote console can monitor multi-robot SLAM on-site and provide direct motion control of the robots.This mobile Ad-WSN complements an environment devoid of usual GPS signals for the robots performing SLAM task in search and rescue environments.In post-disaster areas,the network is usually absent or variable and the site scene is cluttered with obstacles.To adapt to such harsh situations,the proposed self-organizing mobile Ad-WSN enables robots to complete the SLAM process while improving the performances of object of interest identification and exploration area coverage.The information of localization and mapping can communicate freely among multiple robots and remote PC control center via this mobile Ad-WSN.Therefore,the autonomous master robot runs SLAM algorithms while exchanging information with multiple followed-robots and with the remote PC control center via this local WSN environment.Simulations and experiments validate the improved performances of the exploration area coverage,object marked,and loop closure,which are adapted to search and rescue post-disaster cluttered environments.
文摘为提高基于激光雷达的同步定位和建图(simultaneous localization and mapping,SLAM)精度,提出一种基于因子图的高效率、高精度的激光雷达SLAM框架。采用一种基于滑动窗口的因子图方法,将当前帧进行帧间匹配得到相对位姿,按照一定规则选出关键帧,将关键帧与全局地图进行匹配得到绝对位姿;构建一个因子图,将得到的连续帧之间的相对位姿与关键帧的绝对位姿作为优化因子,机器人的位姿作为状态节点放入因子图中进行位姿优化,得到高频率的机器人位姿以及全局一致的环境地图。结果表明:该算法能够减小误差的累积,具有更高的定位精度。
基金Project(ZR2011FM005)supported by the Natural Science Foundation of Shandong Province,China
文摘The task of simultaneous localization and mapping （SLAM） is to build environmental map and locate the position of mobile robot at the same time. FastSLAM 2.0 is one of powerful techniques to solve the SLAM problem. However, there are two obvious limitations in FastSLAM 2.0, one is the linear approximations of nonlinear functions which would cause the filter inconsistent and the other is the ＂particle depletion＂ phenomenon. A kind of PSO ＆ Hjj-based FastSLAM 2.0 algorithm is proposed. For maintaining the estimation accuracy, H~ filter is used instead of EKF for overcoming the inaccuracy caused by the linear approximations of nonlinear functions. The unreasonable proposal distribution of particle greatly influences the pose state estimation of robot. A new sampling strategy based on PSO （particle swarm optimization） is presented to solve the ＂particle depletion＂ phenomenon and improve the accuracy of pose state estimation. The proposed approach overcomes the obvious drawbacks of standard FastSLAM 2.0 algorithm and enhances the robustness and efficiency in the parts of consistency of filter and accuracy of state estimation in SLAM. Simulation results demonstrate the superiority of the proposed approach.
基金Project (No. 2002AA735041) supported by the Hi-Tech Researchand Development Program (863) of China
文摘A novel mobile robot simultaneous localization and mapping (SLAM) method is implemented by using the Rao- Blackwellized particle filter (RBPF) for monocular vision-based autonomous robot in unknown indoor environment. The particle filter combined with unscented Kalman filter (UKF) for extending the path posterior by sampling new poses integrating the current observation. Landmark position estimation and update is implemented through UKF. Furthermore, the number of resampling steps is determined adaptively, which greatly reduces the particle depletion problem. Monocular CCD camera mounted on the robot tracks the 3D natural point landmarks structured with matching image feature pairs extracted through Scale Invariant Feature Transform (SIFT). The matching for multi-dimension SIFT features which are highly distinctive due to a special descriptor is implemented with a KD-Tree. Experiments on the robot Pioneer3 showed that our method is very precise and stable.
基金support from the Key Research and Development Program of Shaanxi(2024GX-ZDCYL-02–04)Technology R&D project of XAUAT Engineering Technology Co.,Ltd.(XAJD-YF24N008)Xi’an University of Architecture and Technology New Urbanization Youth Observation Project(2024GCJH21).
文摘Simultaneous localization and mapping(SLAM)is a crucial technology for construction robots,enabling complex environment mapping and localization at construction sites and facilitating autonomous construction.However,construction sites,particularly those of large public buildings,are characterized by complex spatial structures,timevarying conditions,and dynamic uncertainties.Achieving accurate SLAM in such environments is a highly challenging task for construction robots.In this paper,a SLAM dataset is created specifically for construction sites of large public buildings,and the performance of current mainstream open source 3D light detection and ranging(LiDAR)SLAM algorithms is evaluated.Firstly,an experimental platform for construction robots is established,and a SLAM dataset is generated by collecting data at the construction site of a large public building in Xi’an.Secondly,a simulation environment is developed based on the construction drawings of the ongoing project.A simulation model of the construction robot is created according to the experimental platform,and a SLAM dataset for the simulated construction site environment is compiled by data collection.Finally,comparative experiments involving ten types of open source 3D LiDAR SLAM algorithms are conducted,and the accuracy of SLAM pose estimation and point cloud maps is assessed.The experimental results offer valuable references for SLAM algorithm research for construction robots in construction site environments.Specifically,they reveal the strengths and limitations of existing algorithms under construction-specific challenges,guiding future algorithm optimization.This work not only bridges the gap in construction-oriented SLAM dataset resources but also promotes the practical application of autonomous construction robots in large public building projects.
