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Robust Iterated Sigma Point FastSLAM Algorithm for Mobile Robot Simultaneous Localization and Mapping 认领 引用 被引量：2
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作者 SONG Yu SONG Yongduan LI Qingling 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2011年第4期693-700,共8页
Simultaneous localization and mapping （SLAM） is a key technology for mobile robots operating under unknown environment. While FastSLAM algorithm is a popular solution to the SLAM problem, it suffers from two major d... Simultaneous localization and mapping （SLAM） is a key technology for mobile robots operating under unknown environment. While FastSLAM algorithm is a popular solution to the SLAM problem, it suffers from two major drawbacks： one is particle set degeneracy due to lack of observation information in proposal distribution design of the particle filter; the other is errors accumulation caused by linearization of the nonlinear robot motion model and the nonlinear environment observation model. For the purpose of overcoming the above problems, a new iterated sigma point FastSLAM （ISP-FastSLAM） algorithm is proposed. The main contribution of the algorithm lies in the utilization of iterated sigma point Kalman filter （ISPKF）, which minimizes statistical linearization error through Gaussian-Newton iteration, to design an optimal proposal distribution of the particle filter and to estimate the environment landmarks. On the basis of Rao-Blackwellized particle filter, the proposed ISP-FastSLAM algorithm is comprised by two main parts： in the first part, an iterated sigma point particle filter （ISPPF） to localize the robot is proposed, in which the proposal distribution is accurately estimated by the ISPKF; in the second part, a set of ISPKFs is used to estimate the environment landmarks. The simulation test of the proposed ISP-FastSLAM algorithm compared with FastSLAM2.0 algorithm and Unscented FastSLAM algorithm is carried out, and the performances of the three algorithms are compared. The simulation and comparing results show that the proposed ISP-FastSLAM outperforms other two algorithms both in accuracy and in robustness. The proposed algorithm provides reference for the optimization research of FastSLAM algorithm. 展开更多
关键词 mobile robot simultaneous localization and mapping （SLAM） particle filter Kalman filter unscented transformation
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Research on simultaneous localization and mapping for AUV by an improved method:Variance reduction FastSLAM with simulated annealing 认领 引用 被引量：9
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作者 Jiashan Cui Dongzhu Feng +1 位作者 Yunhui Li Qichen Tian 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2020年第3期651-661,共11页
At present,simultaneous localization and mapping(SLAM) for an autonomous underwater vehicle(AUV)is a research hotspot.Aiming at the problem of non-linear model and non-Gaussian noise in AUV motion,an improved method o... At present,simultaneous localization and mapping(SLAM) for an autonomous underwater vehicle(AUV)is a research hotspot.Aiming at the problem of non-linear model and non-Gaussian noise in AUV motion,an improved method of variance reduction fast simultaneous localization and mapping(FastSLAM) with simulated annealing is proposed to solve the problems of particle degradation,particle depletion and particle loss in traditional FastSLAM,which lead to the reduction of AUV location estimation accuracy.The adaptive exponential fading factor is generated by the anneal function of simulated annealing algorithm to improve the effective particle number and replace resampling.By increasing the weight of small particles and decreasing the weight of large particles,the variance of particle weight can be reduced,the number of effective particles can be increased,and the accuracy of AUV location and feature location estimation can be improved to some extent by retaining more information carried by particles.The experimental results based on trial data show that the proposed simulated annealing variance reduction FastSLAM method avoids particle degradation,maintains the diversity of particles,weakened the degeneracy and improves the accuracy and stability of AUV navigation and localization system. 展开更多
关键词 Autonomous underwater vehicle(AUV) Sonar Simultaneous localization and mapping(SLAM) Simulated annealing FastSLAM
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Simultaneous Localization and Mapping of Autonomous Underwater Vehicle Using Looking Forward Sonar 认领 引用 被引量：3
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作者 曾文静 万磊 +1 位作者 张铁栋 黄蜀玲 《Journal of Shanghai Jiaotong university(Science)》 EI 2012年第1期91-97,共7页
A method of underwater simultaneous localization and mapping(SLAM)based on on-board looking forward sonar is proposed.The real-time data flow is obtained to form the underwater acoustic images and these images are pre... A method of underwater simultaneous localization and mapping(SLAM)based on on-board looking forward sonar is proposed.The real-time data flow is obtained to form the underwater acoustic images and these images are pre-processed and positions of objects are extracted for SLAM.Extended Kalman filter(EKF)is selected as the kernel approach to enable the underwater vehicle to construct a feature map,and the EKF can locate the underwater vehicle through the map.In order to improve the association effciency,a novel association method based on ant colony algorithm is introduced.Results obtained on simulation data and real acoustic vision data in tank are displayed and discussed.The proposed method maintains better association effciency and reduces navigation error,and is effective and feasible. 展开更多
关键词 simultaneous localization and mapping(SLAM) autonomous underwater vehicle(AUV) looking forward sonar extended Kalman filter(EKF)
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Rapid State Augmentation for Compressed EKF-Based Simultaneous Localization and Mapping 认领 引用 被引量：2
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作者 窦丽华 张海强 +1 位作者 陈杰 方浩 《Journal of Beijing Institute of Technology》 EI CAS 2009年第2期192-197,共6页
A new method for speeding up the state augment operations involved in the compressed extended Kalman filter-based simultaneous localization and mapping （CEKF-SLAM） algorithm was proposed. State augment usually requi... A new method for speeding up the state augment operations involved in the compressed extended Kalman filter-based simultaneous localization and mapping （CEKF-SLAM） algorithm was proposed. State augment usually requires a fully-updated state eovariance so as to append the information of newly observed landmarks, thus computational volume increases quadratically with the number of landmarks in the whole map. It was proved that state augment can also be achieved by augmenting just one auxiliary coefficient ma- trix. This method can yield identical estimation results as those using EKF-SLAM algorithm, and computa- tional amount grows only linearly with number of increased landmarks in the local map. The efficiency of this quick state augment for CEKF-SLAM algorithm has been validated by a sophisticated simulation project. 展开更多
关键词 simultaneous localization and mapping （SLAM） extended Kalman filter state augment compu- tational volume
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CF2-SLAM:Conformal-Calibrated Foundation-Factor Graph SLAM across Modalities and Domains 认领 引用
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作者 Xiangqin Chen 《Computers, Materials & Continua》 SCIE EI 2026年第8期1001-1018,共18页
Simultaneous localization and mapping(SLAM)must remain reliable when sensing suites and operating conditions vary across platforms and deployments.Beyond correspondence degradation,a dominant deployment failure mode i... Simultaneous localization and mapping(SLAM)must remain reliable when sensing suites and operating conditions vary across platforms and deployments.Beyond correspondence degradation,a dominant deployment failure mode is misweighted constraints:under distribution shift,uncertainty estimates can become miscalibrated,allowing a small set of overconfident factors to dominate iterative optimization and destabilize inference.This article presents conformal-calibrated foundation-factor graph SLAM(CF2-SLAM),a sensor-agnostic framework that combines frozen foundation representations with lightweight probabilistic factor heads that emit explicit residuals and covariances,and a classical factor-graph back-end for principled multi-modal fusion.To mitigate systematic misweighting under shift,an online conformal calibration layer is introduced to rescale factor covariances by aligning empirical residual quantiles with target quantiles on a per-factor-family basis.Loop closure is further integrated through foundation-descriptor retrieval for candidate proposal and conservative geometric verification for graph insertion,controlling false loop constraints without relying on dataset-specific place-recognition supervision.Across heterogeneous benchmarks spanning monocular,stereo,red-green-blue-depth(RGB-D),and visual-inertial settings,CF2-SLAM operates without retraining and shows improved robustness trends under zero-shot transfer,consistent with stabilized factor weighting. 展开更多
关键词 Simultaneous localization and mapping(SLAM) factor graph optimization foundation models uncertainty estimation online calibration conformal calibration
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Review of Simultaneous Localization and Mapping Technology in the Agricultural Environment 认领 引用 被引量：1
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作者 Yaoguang Wei Bingqian Zhou +3 位作者 Jialong Zhang Ling Sun Dong An Jincun Liu 《Journal of Beijing Institute of Technology》 EI CAS 2023年第3期257-274,共18页
Simultaneous localization and mapping(SLAM)is one of the most attractive research hotspots in the field of robotics,and it is also a prerequisite for the autonomous navigation of robots.It can significantly improve th... Simultaneous localization and mapping(SLAM)is one of the most attractive research hotspots in the field of robotics,and it is also a prerequisite for the autonomous navigation of robots.It can significantly improve the autonomous navigation ability of mobile robots and their adaptability to different application environments and contribute to the realization of real-time obstacle avoidance and dynamic path planning.Moreover,the application of SLAM technology has expanded from industrial production,intelligent transportation,special operations and other fields to agricultural environments,such as autonomous navigation,independent weeding,three-dimen-sional(3D)mapping,and independent harvesting.This paper mainly introduces the principle,sys-tem framework,latest development and application of SLAM technology,especially in agricultural environments.Firstly,the system framework and theory of the SLAM algorithm are introduced,and the SLAM algorithm is described in detail according to different sensor types.Then,the devel-opment and application of SLAM in the agricultural environment are summarized from two aspects:environment map construction,and localization and navigation of agricultural robots.Finally,the challenges and future research directions of SLAM in the agricultural environment are discussed. 展开更多
关键词 simultaneous localization and mapping(SLAM) agricultural environment agricultural robots environment map construction localization and navigation
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Mobile Robot Hierarchical Simultaneous Localization and Mapping Using Monocular Vision 认领 引用 被引量：1
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作者 厉茂海 洪炳熔 罗荣华 《Journal of Shanghai Jiaotong university(Science)》 EI 2007年第6期765-772,共8页
A hierarchical mobile robot simultaneous localization and mapping (SLAM) method that allows us to obtain accurate maps was presented. The local map level is composed of a set of local metric feature maps that are guar... A hierarchical mobile robot simultaneous localization and mapping (SLAM) method that allows us to obtain accurate maps was presented. The local map level is composed of a set of local metric feature maps that are guaranteed to be statistically independent. The global level is a topological graph whose arcs are labeled with the relative location between local maps. An estimation of these relative locations is maintained with local map alignment algorithm, and more accurate estimation is calculated through a global minimization procedure using the loop closure constraint. The local map is built with Rao-Blackwellised particle filter (RBPF), where the particle filter is used to extending the path posterior by sampling new poses. The landmark position estimation and update is implemented through extended Kalman filter (EKF). Monocular vision mounted on the robot tracks the 3D natural point landmarks, which are structured with matching scale invariant feature transform (SIFT) feature pairs. The matching for multi-dimension SIFT features is implemented with a KD-tree in the time cost of O(lbN). Experiment results on Pioneer mobile robot in a real indoor environment show the superior performance of our proposed method. 展开更多
关键词 mobile robot hierarchical simultaneous localization and mapping (SLAM) Rao-Blackwellised particle filter (RBPF) monocular vision scale invariant feature transform
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Underwater Simultaneous Localization and Mapping Based on Forward-looking Sonar 认领 引用 被引量：1
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作者 Tiedong Zhang Wenjing Zeng Lei Wan 《Journal of Marine Science and Application》 2011年第3期371-376,共6页
A method of underwater simultaneous localization and mapping （SLAM） based on forward-looking sonar was proposed in this paper. Positions of objects were obtained by the forward-looking sonar, and an improved associa... A method of underwater simultaneous localization and mapping （SLAM） based on forward-looking sonar was proposed in this paper. Positions of objects were obtained by the forward-looking sonar, and an improved association method based on an ant colony algorithm was introduced to estimate the positions. In order to improve the precision of the positions, the extended Kalman filter （EKF） was adopted. The presented algorithm was tested in a tank, and the maximum estimation error of SLAM gained was 0.25 m. The tests verify that this method can maintain better association efficiency and reduce navigatioJ~ error. 展开更多
关键词 simultaneous localization and mapping （SLAM） looking forward sonar extended Kalman filter （EKF）
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Robust Variational Bayesian Adaptive Cubature Kalman Filtering Algorithm for Simultaneous Localization and Mapping with Heavy-Tailed Noise 认领 引用 被引量：4
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作者 ZHANG Zhuqing DONG Pengu +2 位作者 TUO Hongya LIU Guangjun JIA He 《Journal of Shanghai Jiaotong university(Science)》 EI 2020年第1期76-87,共12页
Simultaneous localization and mapping(SLAM)has been applied across a wide range of areas from robotics to automatic pilot.Most of the SLAM algorithms are based on the assumption that the noise is timeinvariant Gaussia... Simultaneous localization and mapping(SLAM)has been applied across a wide range of areas from robotics to automatic pilot.Most of the SLAM algorithms are based on the assumption that the noise is timeinvariant Gaussian distribution.In some cases,this assumption no longer holds and the performance of the traditional SLAM algorithms declines.In this paper,we present a robust SLAM algorithm based on variational Bayes method by modelling the observation noise as inverse-Wishart distribution with "harmonic mean".Besides,cubature integration is utilized to solve the problem of nonlinear system.The proposed algorithm can effectively solve the problem of filtering divergence for traditional filtering algorithm when suffering the time-variant observation noise,especially for heavy-tai led noise.To validate the algorithm,we compare it with other t raditional filtering algorithms.The results show the effectiveness of the algorithm. 展开更多
关键词 simultaneous localization and mapping(SLAM) variational Bayesian(VB) heavy-tailed noise robust estimation
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Simultaneous Localization and Mapping Technology Based on Project Tango 认领 引用 被引量：3
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作者 XU Pei SU Kehua +1 位作者 HONG Cheng ZHANG Dengyi 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2019年第2期176-184,共9页
Aiming at the problem of system error and noise in simultaneous localization and mapping(SLAM) technology, we propose a calibration model based on Project Tango device and a loop closure detection algorithm based on v... Aiming at the problem of system error and noise in simultaneous localization and mapping(SLAM) technology, we propose a calibration model based on Project Tango device and a loop closure detection algorithm based on visual vocabulary with memory management. The graph optimization is also combined to achieve a running application. First, the color image and depth information of the environment are collected to establish the calibration model of system error and noise. Second, with constraint condition provided by loop closure detection algorithm, speed up robust feature is calculated and matched. Finally, the motion pose model is solved, and the optimal scene model is determined by graph optimization method. This method is compared with Open Constructor for reconstruction on several experimental scenarios. The results show the number of model's points and faces are larger than Open Constructor's, and the scanning time is less than Open Constructor's. The experimental results show the feasibility and efficiency of the proposed algorithm. 展开更多
关键词 simultaneous localization and mapping Project Tango loop closure detection visual vocabulary graph optimization
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Simultaneous Localization and Mapping System Based on Labels 认领 引用 被引量：1
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作者 Tong Liu Panpan Liu +1 位作者 Songtian Shang Yi Yang 《Journal of Beijing Institute of Technology》 EI CAS 2017年第4期534-541,共8页
In this paper a label-based simultaneous localization and mapping（ SLAM） system is proposed to provide localization to indoor autonomous robots. In the system quick response（ QR） codes encoded with serial numbers ... In this paper a label-based simultaneous localization and mapping（ SLAM） system is proposed to provide localization to indoor autonomous robots. In the system quick response（ QR） codes encoded with serial numbers are utilized as labels. These labels are captured by two webcams,then the distances and angles between the labels and webcams are computed. Motion estimated from the two rear wheel encoders is adjusted by observing QR codes. Our system uses the extended Kalman filter（ EKF） for the back-end state estimation. The number of deployed labels controls the state estimation dimension. The label-based EKF-SLAM system eliminates complicated processes,such as data association and loop closure detection in traditional feature-based visual SLAM systems. Our experiments include software-simulation and robot-platform test in a real environment. Results demonstrate that the system has the capability of correcting accumulated errors of dead reckoning and therefore has the advantage of superior precision. 展开更多
关键词 simultaneous localization and mapping （SLAM） extended Kalman filter （EKF） quick response （QR） codes artificial landmarks
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Constrained Submap Algorithm for Simultaneous Localization and Mapping 认领 引用
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作者 钱钧 王晨 +2 位作者 杨明 杨汝清 王春香 《Journal of Shanghai Jiaotong university(Science)》 EI 2009年第5期600-605,共6页
When solving the problem of simultaneous localization and mapping(SLAM) ,a standard extended Kalman filter(EKF) is subject to linearization errors and causes optimistic estimation.This paper proposes a submap algorith... When solving the problem of simultaneous localization and mapping(SLAM) ,a standard extended Kalman filter(EKF) is subject to linearization errors and causes optimistic estimation.This paper proposes a submap algorithm,which builds a weighted least squares(WLS) constraint between two adjacent submaps according to the different estimations of the common features and the relationship between the vehicle poses in the corresponding submaps.By establishing the constraint equation after loop closing,re-linearization is implemented and each submap's reference frame tends to its equilibrium position quickly.Experimental results demonstrate that the algorithm could get a globally consistent map and linearization errors are limited in local regions. 展开更多
关键词 simultaneous localization and mapping （SLAM） consistency submap weighted least squares （WLS）
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A novel method for mobile robot simultaneous localization and mapping 认领 引用 被引量：5
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作者 LI Mao-hai HONG Bing-rong +1 位作者 LUO Rong-hua WEI Zhen-hua 《Journal of Zhejiang University-SCIENCE A》 EI CAS 2006年第6期937-944,共8页
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
关键词 Mobile robot Rao-Blackwellized particle filter （RBPF） Monocular vision Simultaneous localization and mapping （SLAM）
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多模态融合事件相机SLAM技术研究综述 认领 引用 被引量：2
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作者 赵军阳 喻涵 +3 位作者 张志利 祝慧鑫 吕慎华 张克凡 《计算机工程与应用》 EI CSCD 北大核心 2026年第9期1-19,共19页
事件相机是一种新兴的传感器,同时具有高动态范围、低延迟、高时间分辨率、低数据冗余等特点,将事件相机运用到SLAM(simultaneous localization and mapping)技术中能够充分发挥其优势,打破传统传感器使用场景限制。为此,综述了多模态... 事件相机是一种新兴的传感器,同时具有高动态范围、低延迟、高时间分辨率、低数据冗余等特点,将事件相机运用到SLAM(simultaneous localization and mapping)技术中能够充分发挥其优势,打破传统传感器使用场景限制。为此,综述了多模态融合事件相机SLAM技术的研究现状并分析各类方法的特点与缺陷。介绍了SLAM技术与事件相机的基本原理,阐述了事件相机相比传统视觉相机的优势;从单模态事件相机(单目、双目)和多模态融合事件相机(事件与惯性、视觉惯性、多源传感器融合)SLAM技术的角度出发,分析了两者在实时性、应用场景及算力需求上的优缺点并总结了各自的发展历程,而后探讨单一事件相机和多模态融合事件相机对系统性能的影响并给出了部分多模态融合事件相机的性能比较结果。最后,总结了多模态融合事件相机的SLAM技术未来的研究方向,如多模态事件SLAM算法优化与泛化能力提升、多源异构传感器深度融合架构扩展等,以此为事件相机在SLAM领域的应用提供一定的参考与借鉴。 展开更多
关键词 SLAM技术 事件相机 多模态融合 传感器
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Localization and mapping in urban area based on 3D point cloud of autonomous vehicles 认领 引用 被引量：3
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作者 王美玲 李玉 +2 位作者 杨毅 朱昊 刘彤 《Journal of Beijing Institute of Technology》 EI CAS 2016年第4期473-482,共10页
In order to meet the application requirements of autonomous vehicles, this paper proposes a simultaneous localization and mapping （SLAM） algorithm, which uses a VoxelGrid filter to down sample the point cloud data, ... In order to meet the application requirements of autonomous vehicles, this paper proposes a simultaneous localization and mapping （SLAM） algorithm, which uses a VoxelGrid filter to down sample the point cloud data, with the combination of iterative closest points （ICP） algorithm and Gaussian model for particles updating, the matching between the local map and the global map to quantify particles＇ importance weight. The crude estimation by using ICP algorithm can find the high probability area of autonomous vehicles＇ poses, which would decrease particle numbers, increase algorithm speed and restrain particles＇ impoverishment. The calculation of particles＇ importance weight based on matching of attribute between grid maps is simple and practicable. Experiments carried out with the autonomous vehicle platform validate the effectiveness of our approaches. 展开更多
关键词 simultaneous localization and mapping （SLAM） Rao-Blackwellized particle filter （ RB-PF） VoxelGrid filter ICP algorithm Gaussian model urban area
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激光SLAM中动态物体剔除算法研究 认领 引用 被引量：1
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作者 李兆强 苏惠杰 张岳 《计算机工程与应用》 EI CSCD 北大核心 2026年第5期242-251,共10页
同时定位与建图(simultaneous localization and mapping,SLAM)技术是无人驾驶流程中的重要环节,其中建图的精度直接影响到定位、导航以及路径规划等任务,影响精度的关键因素之一是地图中存储的动态物体残影。对此问题,提出一种基于多... 同时定位与建图(simultaneous localization and mapping,SLAM)技术是无人驾驶流程中的重要环节,其中建图的精度直接影响到定位、导航以及路径规划等任务,影响精度的关键因素之一是地图中存储的动态物体残影。对此问题,提出一种基于多目标运动估计(multiple object motion estimation,MOME)对点云进行离线处理的动态物体剔除方法,使用领域图来构建空间中动态物体的运动轨迹,通过帧间观测的变换矩阵作为标签来描述物体的轨迹,用凸优化的方式最小化成本函数,使标签逐步收敛到合适的轨迹。最终通过高斯-牛顿迭代估计状态参数,依据动态物体在雷达坐标系和地固坐标系之间的差异性运动对其分割并剔除。该算法在SemanticKITTI数据集和Argoverse 2数据集的不同场景下进行验证,结果表明,该动态物体剔除方法相比于近年来的经典动态物体剔除方法,具有更优秀的精度和效果。 展开更多
关键词 同时定位与建图(SLAM) 动态环境 多目标运动估计 激光雷达
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弱纹理环境下点线融合鲁棒视觉SLAM算法 认领 引用
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作者 杨官学 刘岳松 +2 位作者 刘慧 沈跃 沈亚运 《计算机工程与应用》 EI CSCD 北大核心 2026年第2期313-324,共12页
针对弱纹理和变光照环境下基于点特征的视觉SLAM(simultaneous localization and mapping)算法轨迹漂移的问题,提出了一种基于改进自适应阈值ELSED算法(Adaptive-ELSED)的快速点线融合双目视觉SLAM算法。通过在ELSED算法中添加自适应阈... 针对弱纹理和变光照环境下基于点特征的视觉SLAM(simultaneous localization and mapping)算法轨迹漂移的问题,提出了一种基于改进自适应阈值ELSED算法(Adaptive-ELSED)的快速点线融合双目视觉SLAM算法。通过在ELSED算法中添加自适应阈值矩阵,动态调整不同光照条件下梯度阈值,并使用长度抑制和短线合并策略,提高线特征的质量。利用基于双目几何约束和图像结构相似性(SSIM)进行快速线段特征三角化。基于历史位姿及误差分析获取初始位姿,通过自适应因子实现光束法平差过程中点线特征的更有效融合。实验结果表明,所提算法在提高线特征质量的同时,耗时仅为LSD算法的50%,线特征匹配速度较传统LBD算法提升67%,挑战性场景下轨迹误差较ORB-SLAM3降低62.2%,系统的平均跟踪帧率为27帧/s,在保证系统实时性的同时,显著提升了系统在弱纹理、变光照环境下的精度和鲁棒性。 展开更多
关键词 双目视觉 弱纹理 视觉同步定位与地图构建(SLAM) 点线特征 特征匹配
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面向智能制造场景的基于神经隐式地图的RGB-D SLAM 认领 引用
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作者 胡乃瑞 朱文林 +2 位作者 李玉峰 安天洋 李光旭 《计算机集成制造系统》 EI CSCD 北大核心 2026年第5期1806-1816,共11页
为了使同步定位和绘图(SLAM)系统能够适应智能制造环境中的场景重建需求,并持续提升智能制造中定位需求的精度和鲁棒性,提出一种基于神经隐式表达的面向智能制造场景的端到端RGB-D SLAM系统,称为NPF-SLAM。该系统采用基于特征的深度神... 为了使同步定位和绘图(SLAM)系统能够适应智能制造环境中的场景重建需求,并持续提升智能制造中定位需求的精度和鲁棒性,提出一种基于神经隐式表达的面向智能制造场景的端到端RGB-D SLAM系统,称为NPF-SLAM。该系统采用基于特征的深度神经网络跟踪器作为前端,并利用NeRF地图构建器作为后端。神经地图构建模块虽然在基于真实智能制造环境采集的Euroc数据集上预训练,但也会随着神经隐式地图构建器的即时训练而进行微调。在此设计下,NPF-SLAM能够在智能制造环境中,通过学习特定场景的特征进行摄像机的精准跟踪,从而实现SLAM系统的终身学习能力。此外,跟踪器和地图构建器的训练均为自监督模式,无需引入姿态真值。在Replica、ScanNet和Euroc等数据集上的实验结果表明,NPF-SLAM在场景重建和摄像机跟踪性能方面优于现有的基于NeRF的SLAM系统,展现了其在复杂智能制造环境中的适应性与先进性。 展开更多
关键词 视觉SLAM 三维重建 姿态估计 地图构建
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基于知识蒸馏的NeRF SLAM模型轻量化研究 认领 引用
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作者 王红星 罗子杰 +5 位作者 吴欢娣 曹雏清 徐劲松 刘国满 邓少波 叶展 《机器人》 EI CSCD 北大核心 2026年第1期116-124,共9页
神经辐射场(NeRF)在高质量3维场景重建方面具有巨大潜力,但其高计算复杂度、数据需求和存储限制使其在实际应用中面临诸多挑战。为了解决这一问题,提出了一种结合知识蒸馏的改进NeRF SLAM系统。通过引入知识蒸馏技术,以实现快速且高效... 神经辐射场(NeRF)在高质量3维场景重建方面具有巨大潜力,但其高计算复杂度、数据需求和存储限制使其在实际应用中面临诸多挑战。为了解决这一问题,提出了一种结合知识蒸馏的改进NeRF SLAM系统。通过引入知识蒸馏技术,以实现快速且高效的训练。实验结果表明,与原始NeRF模型相比,本文的系统在重建精度上使点云准确性提升18.21%、重建点云完整度提升14.86%,完成率提升14.09%,在重建效率上使得总FLOP(浮点运算次数)值下降了35.52%,在保持重建精度的同时,显著减少了训练时间和计算资源消耗。本研究不仅为NeRF SLAM系统的优化提供了新的思路,也为知识蒸馏在3维视觉领域的应用探索了新的途径。 展开更多
关键词 NeRF(神经辐射场) SLAM(同步定位与地图构建) 知识蒸馏 3维场景重建 训练优化
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基于自适应调节机制的激光SLAM后端约束构建方法 认领 引用
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作者 石文博 戴豪杰 +3 位作者 陆昱初 姚陈鹏 刘成菊 陈启军 《机器人》 EI CSCD 北大核心 2026年第1期125-136,共12页
在复杂环境下,几何结构的弱差异性与点云数据特征的降级易引发回环误匹配,导致后端图优化解算误差增加,制约移动机器人地图构建及定位的精确性与可靠性。为此,提出了一种基于自适应调节机制的激光SLAM(同步定位与地图构建)后端约束构建... 在复杂环境下,几何结构的弱差异性与点云数据特征的降级易引发回环误匹配,导致后端图优化解算误差增加,制约移动机器人地图构建及定位的精确性与可靠性。为此,提出了一种基于自适应调节机制的激光SLAM(同步定位与地图构建)后端约束构建方法。首先,设计了基于匹配不确定性的回环检测搜索窗口动态调整方法,通过改进Floyd算法对子图最短距离矩阵的动态更新和维护,实时量化不同匹配节点间的相对不确定性程度,进而根据量化指标设计动态调节机制,自适应调整约束构建过程中的匹配搜索域。其次,提出了基于激光点云特异性的自适应阈值法,通过候选解得分情况量化点云特异性,动态调节约束构建搜索过程中的扫描匹配得分的阈值。最后,在移动机器人平台上进行实体实验,结果表明,与其他主流图优化激光SLAM算法相比,所提方法显著减少了错误约束构建,有效提升了机器人在复杂环境下建图的准确性与定位的鲁棒性。 展开更多
关键词 激光SLAM(同步定位与地图构建) 约束构建 回环检测 图优化 自适应调节机制
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