煤矿井下顾及特征点动态提取的激光SLAM算法研究

LiDAR SLAM algorithm considering dynamic extraction of feature points in underground coal mine

针对煤矿井下无GNSS信号、主流激光SLAM算法易出现特征约束不足而导致退化问题,提出了一种面向煤矿井下环境的激光雷达、IMU紧耦合SLAM算法。首先,设计一种动态提取特征点方法,通过检测煤矿井下环境是否发生退化,动态调整特征点提取数量,构建丰富且良好的特征信息约束矩阵,提高位姿估计准确性;然后,利用因子图优化实现煤矿井下稳健精确的SLAM;最后,通过煤矿井下实测数据进行了广泛的试验分析。结果表明:提出的激光SLAM算法表现较好,位姿估计误差在平面方向较LIO_SAM降低了50.93%,在高程方向降低了42.13%,可为煤矿机器人智能感知、安全巡检提供了技术参考。

Aiming at the problem that there is no GNSS signal in the coal mine,the state-of-the-art LiDAR SLAM algorithm is prone to degenerate due to insufficient feature constraints,a tight coupling SLAM algorithm of LiDAR and IMU for the coal mine environment is proposed.First,we design a dynamic feature point extraction method,by detecting whether there is degradation in the underground environment of coal mine to dynamically adjust the number of feature points extracted,build a rich and good feature information constraint matrix,improve the accuracy of pose estimation;then,the factor diagram optimization is used to realize the robust and accurate SLAM in the coal mine.Finally,a wide range of experimental analysis is carried out through the measured data in the coal mine.The results show that the proposed laser SLAM algorithm performs well,the pose estimation error is reduced by 50.93% in the horizontal direction and 42.13%in the vertical direction compared with LI0_SAM,it can provide technical reference for intelligent perception and safety inspection of coal mine robots.