Cooperative positioning technology based on multi-vehicle information fusion is essential for advanced applications in intelligent transportation systems (ITS). The integration of global navigation satellite systems (GNSS), inertial navigation system (INS), and ultra-wideband (UWB) technology holds significant promise for enhancing the continuity and reliability of vehicle cooperative positioning. In tightly coupled GNSS/INS/UWB integration, the tolerance against measurement outliers and state model perturbations is pivotal for fulfilling the specific requirements of critical ITS applications. To optimize the comprehensive performance of vehicle cooperative positioning under uncertain sensor observation environments, this paper proposes a robust multiple fading factors unscented Kalman filtering (RMFUKF) algorithm based on adaptive cost function. The proposed solution incorporates Huber M-estimation with an adaptive tuning strategy to perform measurement-specific outliers processing. Furthermore, the improved multiple fading factors based on an exponential weighting method are implemented to mitigate the effects of dynamic model mismatches. Experimental results from vehicular field experiments demonstrate that the proposed RMFUKF scheme significantly improves the robustness and adaptive performance of vehicle cooperative positioning under unpredictable, real-world operating conditions.
With the rapid deployment of autonomous micro-UAVs in dynamic environments, path planning must ensure both safety and real-time performance under stringent onboard computational constraints. This paper proposes a dynamic path planning method based on the reciprocal velocity obstacles algorithm, enabling micro-UAVs to safely and efficiently accomplish flight tasks in complex environments. In three-dimensional space, we introduce the Velocity-Obstacle Spherical Crown (VOSC) model to delineate safe and feasible velocity boundaries, thereby ensuring reliable avoidance of moving obstacles. Within this velocity domain, a minimum-deflection-angle replanning strategy generates smooth and dynamically feasible trajectories. For multi-obstacle scenarios, we design a critical-curve-based avoidance scheme that allows the UAV to flexibly select feasible maneuvers along the curve, improving efficiency and robustness. Simulation results demonstrate that, compared with traditional methods, the proposed approach significantly reduces planning time while enhancing trajectory smoothness. Moreover, the algorithm runs online on micro-UAV hardware, highlighting its potential for warehouse navigation, low-altitude urban transport, and other real-time missions.
High-accuracy positioning information plays an important role in the field of autonomous driving, where both the visual and inertial sensors have been widely noticed because of the virtue that they do not rely on external information. However, the accumulation of sensor errors degrades system positioning accuracy in complex environments due to the fixed system model and variable measurement noise in traditional visual inertial odometry (VIO). A novel VIO based on interactive multiple model (IMM) and multistate constrained Kalman filter (MSCKF) is proposed. First, the trifocal tensor model constructed from three consecutive images is used as the measurement model of the system, and the corresponding position and orientation information are added to the filter state vector to form an MSCKF, which is then combined with the IMM to form an IMM-MSCKF algorithm to interactively fuse the inputs and outputs of multiple subfilters to improve the positioning accuracy of the VIO. The proposed method is validated by selected urban environment and highway area data from publicly available datasets. The experimental results show that the proposed algorithm effectively improves the positioning accuracy of integrated navigation while reducing the positioning error of a single sensor compared to the conventional VIO.
Multisensor information fusion has been extensively used in the fields of navigation and localization. Inertial and visual sensors are combined for vehicle navigation in unfamiliar environments, leveraging their complementary strengths. However, during data collection, the complex and dynamic motion environment introduces measurement noise, which reduces positioning accuracy. Traditionally, measurement noise is assumed to be uniformly distributed white Gaussian noise with constant mean and covariance. In practice, however, the measurement noise varies considerably, severely impacting positioning accuracy. To address this issue and enhance the positioning accuracy of visual-inertial navigation systems, this study proposes a monocular visual-inertial odometer based on the adaptive variational Bayes algorithm. This approach accounts for unknown measurement noise by modeling the probability density function (pdf) of the system's measurement noise matrix using the inverse Wishart distribution with a summed mean. This method modifies the Gaussian characteristics of the measurement noise to more accurately represent the real noise. To further enhance system accuracy and avoid measurement bias associated with using only a monocular camera, the high error measurements are mitigated using an innovation chi-square test. This approach reduces the number of iterative approximations while improving accuracy. The proposed algorithm was validated using data from public datasets in various environments. The results demonstrated that the proposed algorithm achieves higher accuracy and better positioning performance compared to the visual-inertial multistate-constrained Kalman filter (MSCKF) fusion algorithm.
Unmanned aerial vehicles (UAVs) have become the focus of current research because of their practicability in various scenarios. However, current local path planning methods often result in trajectories with numerous sharp or inflection points, which are not ideal for smooth UAV flight. This paper introduces a UAV path planning approach based on distance gradients. The key improvements include generating collision-free paths using collision information from initial trajectories and obstacles. Then, collision-free paths are subsequently optimized using distance gradient information. Additionally, a trajectory time adjustment method is proposed to ensure the feasibility and safety of the trajectory while prioritizing smoothness. The Limited-memory BFGS algorithm is employed to efficiently solve optimal local paths, with the ability to quickly restart the trajectory optimization program. The effectiveness of the proposed method is validated in the Robot Operating System simulation environment, demonstrating its ability to meet trajectory planning requirements for UAVs in complex unknown environments with high dynamics. Moreover, it surpasses traditional UAV trajectory planning methods in terms of solution speed, trajectory length, and data volume.
随着社会经济的发展,传统建筑工地正向智慧工地转变.本文分析了传统工地存在的弊端,通过引入物联网技术在智慧工地上的应用,具体分析了在安全、质量、成本、进度、环境与能耗管理上的应用情况,并通过工程实例,介绍了在工地上如何设置各种监控系统以更好的提升信息处理、工程管理模式,推动现有工地向标准化、智慧化、科学化的转变.
针对车辆在城市峡谷、高架桥梁、密林等环境下可见卫星分布不佳导致导航精度变差的问题,提出一种顾及位置精度因子(PDOP)的自适应北斗卫星导航系统(BDS)精密单点定位技术(PPP)/惯性导航系统(INS)紧组合方法.选择消电离层组合模型作为BDS PPP的函数模型,并对影响定位精度和收敛速度的误差项进行分析处理.依据反映卫星分布状况的PDOP,并根据实测数据建立自适应因子函数模型,通过自适应因子实时调节动力学模型降低因可见卫星分布不佳对导航结果的影响,以此保持导航结果的连续性.结果表明:该方法比传统方法具有更高的定位性能,有效提高了BDSPPP/INS组合导航系统在复杂环境下的定位精度.
与传统建筑相比,装配式建筑的主要优点是工业化和标准化生产、节能环保、高效.但在实际施工过程中,装配式建筑或多或少存在质量、安全问题.为从根本上解决这一问题,有效提升建筑品质,保障生命和财产安全,一些企业引进物联网技术,对每个环节进行全方位监控,使现场的安全管理质量明显提高,同时,对装配式建筑的施工质量起到促进作用.文章主要针对物联网技术在装配式建筑施工现场质量和安全管理中的具体应用进行详细分析,总结出应用过程中的有效策略,以更好地推动装配式建筑的发展.
针对以惯性传感器为核心的鞋绑式室内行人导航定位中位置及航向误差无法有效校正的问题,通过集成微机电惯性传感器和室内建筑结构信息,提出一种级联结构的卡尔曼粒子滤波的室内融合定位方法.首先在下级卡尔曼滤波采用零速更新初步修正惯性导航的解算误差;上级粒子滤波器利用室内建筑结构信息通过穿墙检测进一步校准行人位置和航向.实验结果表明,基于级联滤波的建筑结构信息/惯导室内行人导航算法可有效降低惯导误差的累积问题,纠正传统算法采用零速修正校正系统误差时,因为航向不可观测而导致行人轨迹"穿墙"现象,该方法可减小位置更新过程中的误差累计,其结果相比单一惯性行人定位结果的位置均方根误差由0.69 m降低到0.39 m,航向均方根误差由0.81°降低到0.72°.
图像噪声是影响单目视觉定位精度的主要因素.该文在二维经验模态分解(BEMD)和阈值降噪方法的基础上,提出一种基于二维集合经验模态分解(BEEMD)的区间阈值图像噪声滤除方法.图像经过BEEMD分解为不同尺度的多个二维本征模态函数(IMF)分量和1个残余分量,依据图像和IMF分量的2范数准则和概率密度函数方法剔除纯噪声IMF分量,通过合理选择调节因子α,利用改进的区间阈值降噪方法实现图像降噪.将该算法应用于单目视觉测距中,并与BEMD算法进行对比,结果表明,该方法不仅能有效抑制BEMD中的模态混叠问题,而且能有效削弱图像噪声影响,从而提高单目视觉测距的精度和可靠性.
