摘要
矿井安全事故发生后,矿井救援机器人需在矿井环境采集视觉数据,进行应急区域的空间构建等功能。利用RGB和RGB-D的ORB-SLAM2算法进行扩充性优化研究,选用OCTOMAP优化地图,使用矿井救援机器人在室内真实场景中实验,验证该方法效果。结果表明:得到的空间构建效果比实时点云构建的应用性更强,降低处理内存空间,为矿井救援移动机器人空间构建提供方法,对后续矿井机器人移动定位的研究具有重要的实际意义。
After a sudden safety accident in a mine occurs,the mine rescue robot needs to collect visual data in the mine environment and perform functions such as spatial construction of the emergency area.Using the ORB-SLAM2 algorithm of RGB and RGB-D,the scalability optimization research was carried out.OCTOMAP was selected to optimize the map,and the mine rescue robot was used to experiment in the real indoor scene to verify the effect of the method in this paper.The results show that the space construction effect obtained is more applicable than the real-time point cloud construction,reduces the processing memory space,provides a method for the space construction of the mine rescue mobile robot,and has important practical significance for the subsequent research on the mobile positioning of the mine robot.
作者
贯怀光
杨鹏
诸利一
邢怡君
Guan Huaiguang;Yang Peng;Zhu Liyi;Xing Yijun(Beijing Key Laboratory of Information Service Engineering,Beijing Union University,Beijing 100101,China;Urban Rail Transit and Logistics College,Beijing Union University,Beijing 100101,China;School of Civil and Resource Engineering,University of Science and Technology Beijing,Beijing 100083,China)
出处
《计算机应用与软件》
北大核心
2024年第9期70-76,共7页
Computer Applications and Software
基金
国家自然科学基金项目(51774045)。
