Zhang Jitong, Ren Mingrong, Wang Pu, Meng Juan, Mu Yuman
College of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China.
Sensors (Basel). 2020 May 14;20(10):2790. doi: 10.3390/s20102790.
High-precision indoor localization plays a vital role in various places. In recent years, visual inertial odometry (VIO) system has achieved outstanding progress in the field of indoor localization. However, it is easily affected by poor lighting and featureless environments. For this problem, we propose an indoor localization algorithm based on VIO system and three-dimensional (3D) map matching. The 3D map matching is to add height matching on the basis of previous two-dimensional (2D) matching so that the algorithm has more universal applicability. Firstly, the conditional random field model is established. Secondly, an indoor three-dimensional digital map is used as a priori information. Thirdly, the pose and position information output by the VIO system are used as the observation information of the conditional random field (CRF). Finally, the optimal states sequence is obtained and employed as the feedback information to correct the trajectory of VIO system. Experimental results show that our algorithm can effectively improve the positioning accuracy of VIO system in the indoor area of poor lighting and featureless.
高精度室内定位在各个场所都起着至关重要的作用。近年来，视觉惯性里程计（VIO）系统在室内定位领域取得了显著进展。然而，它很容易受到光照不足和无特征环境的影响。针对这个问题，我们提出了一种基于VIO系统和三维（3D）地图匹配的室内定位算法。3D地图匹配是在先前二维（2D）匹配的基础上增加高度匹配，以使算法具有更广泛的适用性。首先，建立条件随机场模型。其次，将室内三维数字地图用作先验信息。第三，将VIO系统输出的位姿和位置信息用作条件随机场（CRF）的观测信息。最后，获得最优状态序列并将其用作反馈信息来校正VIO系统的轨迹。实验结果表明，我们的算法能够有效提高VIO系统在光照不足和无特征室内区域的定位精度。
