Highlights What are the main findings? A physics-geometry dual-driven framework (RI-DVP) is proposed to address the "Sparsity Trap" in 16-beam LiDAR mapping. A dual-residual mechanism (DRAR) combining calibrated intensity and geometric consistency effectively removes dynamic artifacts in sparse scenarios. What are the implications of the main findings? The method improves Dynamic Accuracy (DA) by over 36 percentage points in open environments compared to state-of-the-art geometric baselines. Achieving a processing rate of approximately 15.3 Hz, the framework balances high-fidelity mapping with real-time efficiency for autonomous navigation.Highlights What are the main findings? A physics-geometry dual-driven framework (RI-DVP) is proposed to address the "Sparsity Trap" in 16-beam LiDAR mapping. A dual-residual mechanism (DRAR) combining calibrated intensity and geometric consistency effectively removes dynamic artifacts in sparse scenarios. What are the implications of the main findings? The method improves Dynamic Accuracy (DA) by over 36 percentage points in open environments compared to state-of-the-art geometric baselines. Achieving a processing rate of approximately 15.3 Hz, the framework balances high-fidelity mapping with real-time efficiency for autonomous navigation.Abstract High-fidelity static map construction is essential for reliable autonomous navigation, yet dynamic environments introduce severe artifacts caused by moving objects (also referred to as dynamic artifacts) in accumulated maps. While geometry-based methods perform well on dense point clouds, their performance notably degrades on sparse 16-beam LiDAR due to the "Sparsity Trap": dynamic objects are frequently missed by ray-based geometry, and purely geometric cues fail in radiometrically ambiguous scenarios. To address this, we propose RI-DVP, a physics-geometry dual-driven framework. Unlike conventional approaches, RI-DVP first performs a physics-inspired radiometric normalization that compensates for range attenuation and incidence-angle effects to establish a consistent signal baseline. Subsequently, a Dual-Residual Aggressive Removal (DRAR) module jointly exploits geometric residuals-bounded by a range-dependent spatial uncertainty envelope-and calibrated intensity residuals to detect geometrically indistinguishable objects. To balance recall and precision, a Hierarchical Static Reversion strategy (HSR) employs two-stage recovery to retrieve large-scale structures and correct fine-grained artifacts via topology-based adhesion reasoning. Experiments on SemanticKITTI and custom sparse datasets demonstrate that RI-DVP outperforms state-of-the-art geometric baselines, improving Dynamic Accuracy by over 36 percentage points in sparse scanning scenarios using a VLP-16 LiDAR sensor (Velodyne Acoustics, Inc., Morgan Hill, CA, USA) compared to baselines that fail under the sparsity trap while achieving real-time performance at approximately 15.3 Hz.

Navigation technology, which integrates vision, Inertial Measurement Unit (IMU), and Ultra-Wideband (UWB) sensors in GNSS-denied environments has gained a significant attention. However, inaccurate estimation of UWB anchor positions and improper sensor weighting among heterogeneous sensors significantly impairs the positioning accuracy and robustness of Visual-Inertial-UWB (VIU) systems. To accurately and rapidly estimate the UWB anchor positions, we employed the robust ridge nonlinear least-squares method to improve the accuracy and reliability of the estimated UWB anchor position. Additionally, we proposed a simple and effective method to assess the accuracy of the UWB anchor position using the geometric dilution precision principle, which facilitates rapid and accurate estimation of the UWB anchor position. Furthermore, we designed a method to calculate the estimated UWB anchor position error in real-world settings. Finally, we proposed a nonlinear optimization method with dynamically adaptive weighting based on the HELMERT variance component estimation principle, which assigns appropriate weights to heterogeneous sensors. To validate the feasibility and effectiveness of the proposed method, comprehensive simulations and real-world experiments were conducted. First, using Monte Carlo simulation and real-world experiments, we validated the effectiveness of the proposed methods for UWB anchor position and its accuracy estimation. Then, we conducted ablation experiments utilizing the open-source VIRAL and real-world datasets. The experimental results demonstrate that the proposed method exhibits superior positioning accuracy and robustness in contrast to the open-source VINS-MONO and VIR-SLAM methods.

In global satellite navigation system (GNSS)-denied environments, unmanned aerial vehicle (UAV) navigation based on visual–inertial odometry (VIO) has been widely studied. However, existing VIO methods still suffer from some practical problems, such as image enhancement oversaturation and unreasonable weighting in backend optimization. Therefore, this article presents monocular VIO with point-line fusion and backend adaptive optimization to improve the positioning accuracy and robustness of UAV navigation systems. In the front end, we proposed an adaptive gamma image correction algorithm for image preprocessing to avoid image oversaturation, which is more conducive to image extraction and matching. Instead of the traditional line segment detector (LSD) line feature extraction algorithm, we employed an improved EDLines algorithm to enhance the efficiency of line feature extraction, better meeting the high dynamic real-time requirements of UAV. In the backend, we proposed a tightly coupled nonlinear adaptive optimization method based on a two-step approach to address the issue of unreasonable static weights. In the first step, we established a factor graph model and performed the first nonlinear optimization based on a priori visual weights. In the second step, we calculated the reprojection error and established a functional model that examines the relationship between the reprojection error and the information matrix. We updated the information matrix using the reprojection error to adaptively adjust the weights of the point features and line features in real time. Finally, we performed a second nonlinear reoptimization. The proposed method was compared with the monocular visual-inertial system (VINS-MONO) (Qin et al. 2018) and point and line features (PL)-VINS (Fu et al. 2020) methods, the experimental results showing that the positioning accuracy of the proposed method on the public EuRoc dataset (Burri et al. 2016) improved by an average of 32.3% compared with the PL-VINS method, and by an average of 33.8% in three real-world scenarios under changing illumination, weak texture, and large-scale complex scenarios. The results demonstrated that the proposed method exhibited better robustness and higher positioning accuracy in various complex environments.

Optical motion capture systems are devices used to track and capture target movements and obtain the position and attitude of the target in real time.In practical tasks such as UAV obstacle avoidance,vehicle-machine co-positioning and multi-machine co-operation,optical motion capture systems are required to provide reliable and high-precision positioning coordinates for targets.On the one hand,the optical motion capture system provides high-precision positioning coordinates for the target to assist in tasks such as navigation and obstacle avoidance;on the other hand,the optical motion capture system provides high-precision positioning true values for the target to verify the accuracy of the positioning and navigation algorithms.However,the actual positioning accuracy of the optical motion capture system is often much lower than the manufacturer's nominal accuracy due to factors such as the camera distribution,number of cameras in the optical motion capture system and vibrations in the field.Therefore,it is urgent and necessary to verify the actual positioning accuracy of optical motion capture systems in large scenes.Especially in large scenes,optical motion capture systems suffer from scattered camera distribution,a small number of cameras in the common viewing area,and long camera-target distances,resulting in inaccurate positioning.If the actual positioning accuracy of the optical motion capture system is significantly different from the nominal accuracy,the performance of the motion capture system will be greatly reduced and it will be difficult for the motion capture system to perform its actual function.In general,the positioning accuracy of the optical motion capture system is mainly tested in small spaces,and the positioning accuracy of the system is often assessed in terms of repeated trajectory accuracy,resulting in the actual positioning accuracy of optical motion capture systems in real scenes,especially in large scenes,being unknown or inaccurate.To solve the problem of precision detection of the optical motion capture system in large scenes,a method of calibrating the positioning accuracy of the optical motion capture system in large scenes using total station is proposed.First,select several common points evenly in the four sub-regions where the optical motion capture system is located.Second,the concentric target ball workpiece of the same size is used to solve the problem that the measuring points of the total station and the optical motion capture system are inconsistent.Third,the real value and measured value of the common point are obtained through the measurement of the total station and optical motion capture system and the position and posture transformation matrix between the total station coordinate system and the optical motion capture system coordinate system is calculated based on robust least squares iterative algorithm for Rodrigues matrix.Finally,the positioning accuracy of the optical motion capture system in the sub-region and the whole region is calculated under the unified coordinate system.Meanwhile,the external parameter calibration of the measurement system and the Vicon system is realized based on the above method.The experimental results show that the positioning accuracy of the four sub-region optical motion capture systems is 2.385 mm,0.877 mm,1.787 mm,2.890 mm respectively,and the positioning accuracy of the optical motion capture system in the whole region is 8.126 mm.It shows that the positioning accuracy of the optical motion capture system is significantly reduced in large scenes,and the feasibility and effectiveness of using the total station to calibrate the optical motion capture system and external parameters of the motion capture system are verified.With the increasing application demand and positioning accuracy requirements for optical motion capture systems,the proposed method is an inspiration and reference for future calibration of optical motion capture systems in large and complex scenes.

在 GNSS 信号拒止的室内环境定位中,针对视觉惯性里程计(VIO)在长期运动或无回环等不利环境下产生误差累积偏移以及超宽带(UWB)受非视距影响定位精度难以保证的问题,提出了一种基于抗差估计的UWB辅助视觉惯性自适应组合定位算法.首先,构建UWB与VIO的优化框架,利用UWB定位结果对VIO进行全局约束.其次,在后端优化阶段加入抗差估计,实时调整传感器间的权重值,抑制UWB非视距的影响.最后,在EuRoc数据集和真实场景中进行了实验验证.真实场景实验结果表明,组合算法在非遮挡条件下定位精度相比于VINS_Mono提高 75.05%,基于抗差估计的组合算法在遮挡条件下定位精度相比于不加抗差估计的组合算法提高 37.53%.

To improve localization and pose precision of visual–inertial simultaneous localization and mapping (viSLAM) in complex scenarios, it is necessary to tune the weights of the visual and inertial inputs during sensor fusion. To this end, we propose a resilient viSLAM algorithm based on covariance tuning. During back-end optimization of the viSLAM process, the unit-weight root-mean-square error (RMSE) of the visual reprojection and IMU preintegration in each optimization is computed to construct a covariance tuning function, producing a new covariance matrix. This is used to perform another round of nonlinear optimization, effectively improving pose and localization precision without closed-loop detection. In the validation experiment, our algorithm outperformed the OKVIS, R-VIO, and VINS-Mono open-source viSLAM frameworks in pose and localization precision on the EuRoc dataset, at all difficulty levels.

针对在全球卫星导航系统(GNSS)及超宽带(UWB)组合的室内外无缝定位系统中,室内定位基准取决于UWB基站的位置基准,与GNSS通常所用的大地坐标系不一致的问题,提出一种基站位置估计方法:利用携带GNSS接收机和UWB标签的移动站来估计室内UWB基站位置;以移动站获取的GNSS坐标和UWB测距信息作为观测量,采用扩展卡尔曼滤波(EKF)法解算UWB基站坐标,使UWB定位基准自然统一到大地坐标系下.实验结果表明:该方法具有较好的估计效果;UWB基站无需提前布设,自然统一到大地坐标系下.

针对多激光雷达外参数的标定问题,提出一种利用平面匹配的激光雷达间相对位姿自动标定方法.首先利用RANSAC算法对回廊式结构进行平面和相应法向量的提取;其次通过一系列的方法匹配对应平面,采用Kabsch算法求解初始外参数;最后利用Ceres 库进行非线性优化,实现了较高精度的激光雷达外参数标定.仿真实验以及实际测试结果表明,该方法具有较高的标定精度和较强的鲁棒性,旋转、平移误差分别小于0.03 rad和0.04 m.

为了解决机器人工具坐标系标定时标定精度不高、效率较低的问题,提出一种基于激光跟踪仪的工具坐标系快速标定方法。首先,分析机器人末端法兰盘的结构,根据法兰盘上各点的相对位置关系,利用激光跟踪仪及几何法原理进行工具坐标系的位置标定;其次,控制机器人沿工具坐标系的X轴和Z轴方向分别运动,根据工具坐标系和法兰末端坐标系的相对位姿关系进行工具坐标系姿态标定;最后,利用基于距离约束的工具坐标系标定方法和本文提出的几何法标定结果进行精度对比分析。实验结果表明几何法进行工具坐标系后机器人定位精度能达到0.692 mm,与距离约束法的工具坐标系标定精度相当;同时几何法不需要机器人运动,只需测量机器人末端的6个点就能实现工具坐标系的位置标定,且机器人末端更换新的工具后只需测量一个点就能实现新工具的位置标定。实验表明,几何法标定实验过程为3min,而距离约束法实验过程为8 min,标定效率提高了62.5%,说明该方法具有高效率和高精度的特点,能够满足高精度任务作业的实际需要。

超宽带UWB(Ultra Wide Band)技术是一种新兴的室内定位技术,具有功耗低、定位精度高、穿透能力强等特点,被广泛研究.利用到达时间差TDOA(Time Difference of Arrival)测量方法,通过UWB标签向多个基站发送信号得到标签到各个基站的时间戳差,利用Chan算法实时解算标签的运动位置.建立"当前"统计模型,设置阈值改进自适应卡尔曼滤波迭代过程,利用改进后的自适应卡尔曼滤波算法消除非视距误差和噪声对TDOA定位的影响,实现了更准确的TDOA室内定位.实验表明,基于自适应卡尔曼滤波的TDOA定位方法具有较高的精度.

机器人工具坐标系标定的好坏直接决定机器人工作的可靠性和准确度,准确地标定机器人工具坐标系,能有效提高机器人完成任务的效率,满足实际需要.基于最小二乘原理,将位置和姿态分开标定,操作机器人使工具末端以四种不同的姿态到达同一个位置,根据示教器上的读数解算得到工具坐标系的位置;使机器人工具末端分别沿基坐标系的X轴和Z轴正方向运动,解算得到工具坐标系相对于法兰末端坐标系的姿态.最后通过MATLAB进行仿真验证,得到工具末端的位置误差在0.1mm以内,说明该方法具有较高的精度.

针对超宽带(UWB)定位过程中,易出现基站距离信息不足无法准确定位的问题,提出1种适用于UWB定位的集中式解算方法:介绍3种UWB定位估计算法并比较其优缺点,然后分析并推导2种定位模型的解算方程,最后提出协作式定位解算策略.实验结果表明,在基站距离信息不足的情况下,采用协作定位模型仍然能解算出坐标,而相比单点定位方法,精度由厘米级下降到分米级.

同时定位与地图构建(SLAM)系统在机器人、自动驾驶和移动测量等领域有广泛的应用,但目前系统精度的测试方法尚不完善.针对SLAM系统精度测试的实际需求,本文提出了一种SLAM高精度测试场的建立方法.首先建立三维控制网,然后利用三维激光扫描技术对环境信息进行采集,最终构建出用于测试SLAM系统精度的高精度三维点云特征库.试验结果表明,建立的三维点云特征库包含丰富的特征信息,具有真实地理坐标,精度达到毫米级,可以较好地满足SLAM精度测试的需求.

Nowadays, the position and posture of moving objects are playing more and more important roles in space flight, navigation, target tracking and so on. It is crucial to obtain high-precision position and posture of the carriers. Using multiple laser trackers, high-precision position and posture of the carriers can be obtained, but there are still some errors due to the influence of noise and other factors. In this paper, a current statistical model is built, the Kalman filter adaptive algorithm is designed to filter the sampling data of the three laser trackers, removing the influence of noise and other factors on the laser tracking system and obtaining more accurate sampling coordinates. Besides, the three-spline interpolation method is used to obtain the position coordinates of the carrier at any time. Finally, the high-precision position and posture of the carrier at any time are obtained through the calculation of position and posture.