Beijing Key Laboratory of High Dynamic Navigation Technology
School of Automation, Beijing University of Technology

【目的】针对现有双目视觉/惯性里程计算法在遮蔽空间下救援人员进行定位计算时无法实时精准捕捉数据的问题,提出了一种迭代自适应多状态约束卡尔曼滤波双目视觉/惯性里程计算法(NN-MSCKF)。【方法】首先分析遮蔽空间下救援人员剧烈、复杂运动的跟踪效率和实时性需求,设计迭代自适应算法,利用窗口数据迭代对激励进行判断,触发初始化条件构造量测更新;其次研究地图点个数和像素区分度评估与筛选方式,引入地图点优化机制,提高对地图点进行评估和筛选的实时性;最后搭建仿真与试验平台对算法进行验证。【结果】实验结果表明,该算法相比MSCKF算法实时性提高1s,全局精度提升55%,局部精度提升88.9%,验证了本方法的有效性。

【Purposes】An iterative adaptive multi-state constrained Kalman filter binocular vi⁃ sion/inertial mileage calculation method (NN-MSCKF) is proposed to address the problem that the existing binocular vision/inertial mileage calculation method cannot accurately capture data in real time when the rescuers are performing localization calculations in obscured space. 【Methods】First, the tracking efficiency and real-time requirements of the rescue personnel’s violent and complex move⁃ ments in occluded space analyzed, an iterative adaptive algorithm was designed, and window data it⁃ eration was used to judge the excitation and trigger the initialisation condition to construct the measure⁃ ment update; Second, the way of evaluating and screening the number of map points and pixel differen⁃ tiation was studied, and a map point optimisation mechanism was introduced to improve the real-time performance of evaluating and screening map points; Finally, a simulation and test platform is built to validate the algorithm. 【Findings】The experimental results show that the algorithm improves the real-time performance by 1s, the global accuracy by 55% and the local accuracy by 88.9% compared with the MSCKF algorithm, which verifies the effectiveness of the method.

国家重点研发计划课题(2020YFC1511702);北京市自然科学基金(4212003);高动态导航技术北京市重点实验室资助

节笑晗,刘宁,沈凯,等.基于迭代自适应的多状态约束视觉/惯性融合定位算法[J].太原理工大学学报,2025,56(2):356-364.

JIE Xiaohan,LIU Ning,SHEN Kai,et al.Iterative adaptive multi-state constrained localization algorithm based on vision/inertial fusion[J].Journal of Taiyuan University of Technoloty,2025,56(2):356-364.