魏彤, 金砺耀. 基于双目ORB-SLAM的障碍物记忆定位与去噪算法[J]. 机器人, 2018, 40(3): 266-272. DOI: 10.13973/j.cnki.robot.170403
引用本文: 魏彤, 金砺耀. 基于双目ORB-SLAM的障碍物记忆定位与去噪算法[J]. 机器人, 2018, 40(3): 266-272. DOI: 10.13973/j.cnki.robot.170403
WEI Tong, JIN Liyao. Obstacle Memory Localization and Denoising Algorithm Based on Binocular ORB-SLAM[J]. ROBOT, 2018, 40(3): 266-272. DOI: 10.13973/j.cnki.robot.170403
Citation: WEI Tong, JIN Liyao. Obstacle Memory Localization and Denoising Algorithm Based on Binocular ORB-SLAM[J]. ROBOT, 2018, 40(3): 266-272. DOI: 10.13973/j.cnki.robot.170403

基于双目ORB-SLAM的障碍物记忆定位与去噪算法

Obstacle Memory Localization and Denoising Algorithm Based on Binocular ORB-SLAM

  • 摘要: 针对现有视觉障碍物定位算法无法定位移出视野的障碍物且存在定位噪声的问题,提出一种基于双目ORB-SLAM (基于ORB特征的同时定位与地图构建系统)的障碍物记忆定位与去噪算法.算法在障碍物识别的基础上,首先将逐帧障碍物像点通过SLAM (同步定位与地图创建)地图投影到地面栅格,然后计算栅格内标准障碍物投影点数,进而采用大津(Otsu)法去除定位噪声,最终得到准确的障碍物记忆定位结果.实验结果显示,障碍物移出视野后仍能被记忆定位,单一障碍物去噪成功率达到95.3%,并且平均处理速度达到每秒8个关键帧.这证明本文算法实现了障碍物记忆定位,具有良好的去噪性能及实时性.

     

    Abstract: The existing visual obstacle localization algorithms can't locate the obstacles moving out of sight, and there are noises in localization. For these problems, an obstacle memory localization and denoising algorithm based on binocular ORB-SLAM (simultaneous localization and mapping system based on the oriented BRIEF feature) is proposed. Based on the detection results of the obstacle, the algorithm projects the obstacle points to the ground grid through the SLAM (simultaneous localization and mapping) map frame-by-frame firstly, calculates the number of standard obstacle projection points in the grid, and removes the localization noises by the Otsu method. So, the accurate obstacle memory position is obtained finally. The experimental results show that the obstacle can still be localized after moving out of sight, the denoising success rate of a single obstacle is 95.3%, and the average processing speed reaches 8 key frames per second. It is proved that the algorithm can realize the obstacle memory localization, and has good denoising performance and real-time performance.

     

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