The purpose of this study is to use visual and inertial sensors to achieve real-time location. How to provide an accurate location has become a popular research topic in the field of indoor navigation. Although the complementarity of vision and inertia has been widely applied in indoor navigation, many problems remain, such as inertial sensor deviation calibration, unsynchronized visual and inertial data acquisition and large amount of stored data.,First, this study demonstrates that the vanishing point (VP) evaluation function improves the precision of extraction, and the nearest ground corner point (NGCP) of the adjacent frame is estimated by pre-integrating the inertial sensor. The Sequential Similarity Detection Algorithm (SSDA) and Random Sample Consensus (RANSAC) algorithms are adopted to accurately match the adjacent NGCP in the estimated region of interest. Second, the model of visual pose is established by using the parameters of the camera itself, VP and NGCP. The model of inertial pose is established by pre-integrating. Third, location is calculated by fusing the model of vision and inertia.,In this paper, a novel method is proposed to fuse visual and inertial sensor to locate indoor environment. The authors describe the building of an embedded hardware platform to the best of their knowledge and compare the result with a mature method and POSAV310.,This paper proposes a VP evaluation function that is used to extract the most advantages in the intersection of a plurality of parallel lines. To improve the extraction speed of adjacent frame, the authors first proposed fusing the NGCP of the current frame and the calibrated pre-integration to estimate the NGCP of the next frame. The visual pose model was established using extinction VP and NGCP, calibration of inertial sensor. This theory offers the linear processing equation of gyroscope and accelerometer by the model of visual and inertial pose.

中文翻译：

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使用视觉惯性传感器进行室内导航的实时位置估计

本研究的目的是利用视觉和惯性传感器来实现实时定位。如何提供准确的位置已成为室内导航领域的热门研究课题。视觉与惯性互补虽然在室内导航中得到了广泛应用，但仍存在许多问题，如惯性传感器偏差校准、视觉与惯性数据采集不同步、存储数据量大等。 VP)评价函数提高了提取精度，通过预积分惯性传感器估计相邻帧的最近地面角点(NGCP)。采用顺序相似性检测算法（SSDA）和随机样本一致性（RANSAC）算法在估计的感兴趣区域内准确匹配相邻的NGCP。其次，利用相机本身的参数VP和NGCP建立视觉姿态模型。通过预积分建立惯性位姿模型。第三,通过融合视觉和惯性模型计算位置。,本文提出了一种融合视觉和惯性传感器的室内环境定位方法。作者尽其所知描述了嵌入式硬件平台的构建，并将结果与​​成熟的方法和 POSAV310 进行了比较。，本文提出了一种 VP 评估函数，用于在多个交叉点的交集中提取最大优势。平行线。为了提高相邻帧的提取速度，作者首先提出融合当前帧的NGCP和校准后的预积分来估计下一帧的NGCP。使用消光VP和NGCP，惯性传感器标定建立视觉姿态模型。该理论通过视觉和惯性姿态模型给出了陀螺仪和加速度计的线性处理方程。