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何坚, 刘新远. RGB-D和惯性传感器融合的地面障碍物检测技术[J]. 计算机辅助设计与图形学学报, 2022, 34(2): 254-263. DOI: 10.3724/SP.J.1089.2022.18870
引用本文: 何坚, 刘新远. RGB-D和惯性传感器融合的地面障碍物检测技术[J]. 计算机辅助设计与图形学学报, 2022, 34(2): 254-263. DOI: 10.3724/SP.J.1089.2022.18870
He Jian, Liu Xinyuan. Ground Obstacle Detection Technology Based on Fusion of RGB-D and Inertial Sensors[J]. Journal of Computer-Aided Design & Computer Graphics, 2022, 34(2): 254-263. DOI: 10.3724/SP.J.1089.2022.18870
Citation: He Jian, Liu Xinyuan. Ground Obstacle Detection Technology Based on Fusion of RGB-D and Inertial Sensors[J]. Journal of Computer-Aided Design & Computer Graphics, 2022, 34(2): 254-263. DOI: 10.3724/SP.J.1089.2022.18870

RGB-D和惯性传感器融合的地面障碍物检测技术

Ground Obstacle Detection Technology Based on Fusion of RGB-D and Inertial Sensors

  • 摘要: 针对视障人士出行辅助中可通行区域地面障碍物实时检测问题,提出一种基于RGB-D和惯性传感器融合的地面障碍物检测技术.首先建立地面障碍物空间模型,并融合惯性传感器参数计算相机倾角以校正地面障碍物世界坐标;其次针对视障人士实际使用场景和需求,使用阈值分割算法将深度图像中距离较远的检测像素去除,并将深度图划分4个区域,通过融合惯性传感器数据实现ROI的动态划分;最后通过改进RANSAC算法设计了基于地面区域生长的障碍物检测算法,并采集真实数据进行实验验证.实验结果表明,所提技术的准确率和召回率分别达到90.87%和89.33%,并在执行时间效率上优于已有地面障碍物检测算法,满足了视障人士对算法的实时性要求.

     

    Abstract: Aiming at the real-time detection of ground obstacles in the passable area for the visually impaired, a ground obstacle detection technology based on the fusion of RGB-D and inertial sensor is proposed. Firstly, the spatial model of the ground obstacle is established, and the inertial sensor parameters are fused to calculate the camera inclination to correct the world coordinates of the ground obstacle. Secondly, according to the actual scenes and needs of the visually impaired, the remote detection pixels in the depth image are removed by threshold segmentation algorithm, and the depth map is divided into 4 regions, and the dynamic division of ROI is realized by fusing inertial sensor data. Finally, an obstacle detection algorithm is designed by improved RANSAC algorithm based on the growth of ground area algorithm, and collected real data for experimental verification. The experimental results show that the accuracy and recall rates of the proposed technology reach 90.87% and 89.33%, and better than the existing detection algorithms in the time efficiency of execution, which meets the real-time requirements of the algorithm for the visually impaired.

     

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