基于激光SLAM的复杂场景智能机器人位置自标定研究  

Research on position self calibration of intelligent robots in complex scenes based on laser SLAM

作　　者：李丽[1] LI Li(College of Mechanical And Electrical Engineering,Xinxiang University,Xinxiang Henan 453000,China)

机构地区：[1]新乡学院机电工程学院,河南新乡453000

出　　处：《激光杂志》2025年第1期228-233,共6页Laser Journal

基　　金：河南省自然科学基金项目(No.202100330213)。

摘　　要：机器人技术发展快速,已经在各种复杂场景中广泛应用。然而,这些场景通常是动态变化的,存在光照不均、遮挡等影响,导致机器人的定位精度受到影响。因此,提出基于激光SLAM的复杂场景智能机器人位置自标定研究。利用覆盖栅格地图、后验概率构建机器人移动地图,为了保证机器人位置自标定结果精度,引入粒子滤波算法,通过重要性采样原理获取位置状态样本集,根据位置状态转移函数、传感器观测原理得出机器人位置状态先验概率,采用贝叶斯公式更新先验概率,得到后验概率,通过蒙特卡洛、狄拉克函数更新位置状态权重,使用重采样剔除位置状态权值退化部分,经多次迭代运算得出最优标定结果。实验结果表明,激光SLAM能够复杂场景实现智能机器人位置自标定,且定位误差小、收敛速度速度快。The rapid development of robotics technology has led to its increasingly widespread application in various complex scenarios.However,these scenes are usually dynamic and subject to effects such as uneven lighting and occlusion,which can affect the positioning accuracy of robots.Therefore,a research on position self calibration of intelligent robots in complex scenes based on laser SLAM is proposed.Using coverage grid map and posterior probability to construct a robot movement map,in order to ensure the accuracy of robot position self calibration results,particle filtering algorithm is introduced.The position state sample set is obtained through importance sampling principle,and the prior probability of robot position state is obtained based on the position state transition function and sensor observation principle.Bayesian formula is used to update the prior probability and obtain the posterior probability.Monte Carlo simulation is used to The Dirac function updates the position state weights,uses resampling to remove the degraded parts of the position state weights,and obtains the optimal calibration result through multiple iterations.The experimental results show that laser SLAM can achieve intelligent robot position self calibration in complex scenes,with small positioning error and fast convergence speed.

关 键 词：激光SLAM 智能机器人 位置自标定 概率分布 状态权重 后验概率 

分 类 号：TN242[电子电信—物理电子学]