一种侦察机器人的SLAM关键帧自适应选取算法

An Adaptive Selection Algorithm of SLAM Key Frame for Reconnaissance Robots

针对目前RGB-D SLAM算法中关键帧的选取速度较慢,从而导致侦察机器人的即时定位和全局地图构建过程中出现即时性较差的问题,提出了一种适用于RGB-D相机的SLAM关键帧自适应选取算法。机器人不同的运动状态会使相机获取的连续图像的帧间相似度发生变化,从而影响关键帧的选取。利用神经网络构建了机器人运动状态与关键帧步长的关系模型,提出基于机器人运动状态的关键帧自适应选取算法,实现关键帧的自适应选取。实验结果表明,该算法降低了系统的计算量,提高了系统整体的运行效率,因此所提算法可以在保证准确度的同时有效提升SLAM系统的运行速度,具有较强的实用性。

Aiming at the problem that the reconnaissance robot has poor immediacy in the real-time positioning and global map construction process because of the slow selection of key frames in the RGB-D SLAM algorithm,this paper proposes a SLAM key frame adaptive selection algorithm suitable for RGB-D cameras.The inter-frame similarity of the continuous images acquired by the camera may be changed due to the different motion states of the robot,which will affect the selection of key frames.In this paper,the neural network is used to construct the relationship model between the motion state of the robot and the step length of the key frame.The key frame adaptive selection algorithm based on the motion state of the robot is proposed to realize the adaptive selection of key frames.The experimental results show that,the algorithm reduces the amount of calculation of the system and improves the overall operating efficiency of the system.Therefore,the algorithm can effectively improve the running speed of the system while ensuring the accuracy and has strong practicability.