基于平面扩展和约束优化的激光惯性SLAM方法  

A laser inertial SLAM approach based on planar expansion and constrained optimization

作　　者：胡文学 王泽华 余成 杨奎 梁冬泰[1] Hu Wenxue;Wang Zehua;Yu Cheng;Yang Kui;Liang Dongtai(School of Mechanical Engineering and Mechanics,Ningbo University,Ningbo,Zhejiang 315211,China;Quanhang Technology Co.,Ltd.,Ningbo,Zhejiang 315100,China)

机构地区：[1]宁波大学机械工程与力学学院,浙江宁波315211 [2]诠航科技有限公司,浙江宁波315100

出　　处：《光电工程》2024年第4期18-28,共11页Opto-Electronic Engineering

基　　金：浙江省公益技术应用研究计划项目(LGG21E050008);宁波市科技创新2025重大专项(2022Z075);宁波市公益性科技计划项目(2022S004)。

摘　　要：针对激光SLAM算法在特征匮乏、拐角狭窄的室内场景中定位精度低的问题,提出一种基于平面扩展和约束优化的激光惯性SLAM方法。在激光SLAM中融合IMU,根据IMU状态估计结果对激光点云进行位置补偿并判断关键帧。搭建全局平面地图,基于RANSAC算法对关键帧进行平面提取并结合预提取的方法跟踪平面特征以降低时间成本,拟合结果经iPCA优化去除噪声对RANSAC的影响。利用点到面的距离构建平面约束优化方程,并将其与边缘点约束和预积分约束统一融合,建立非线性优化模型,求解得到优化后的平面信息和关键帧位姿。最后为验证算法的有效性,在M2DGR公开数据集和私有数据集上分别进行实验,实验结果表明,本算法在大部分公开数据集上表现良好,特别在私有数据集上,相比于目前广泛应用的faster-lio算法,定位精度提升61.9%,展现出良好的鲁棒性和实时性。Aiming at the problem of low positioning accuracy of laser SLAM algorithm in indoor scenes with feature scarcity and narrow corners,a laser inertial SLAM method based on planar extension and constraint optimization is proposed.The IMU is fused in laser SLAM,and the laser point cloud is position compensated and key frames are judged according to the IMU state estimation results.The global planar map is constructed,the planar extraction of key frames is performed based on the RANSAC algorithm and combined with the pre-extraction method to track the planar features in order to reduce the time cost,and the fitting results are optimized by iPCA to remove the effect of noise on the RANSAC.Using the distance from the point to the surface to construct the plane constraint optimization equation,and integrate it with the edge point constraints and pre-integration constraints in a unified way to establish a nonlinear optimization model,and solve to get the optimized plane information and key frame bit position.Finally,to verify the effectiveness of the algorithm,experiments are carried out on the M2DGR public dataset and private dataset respectively,and the experimental results show that the present algorithm performs well on most of the public datasets,especially in the private dataset compared with the widely used fast-lio algorithm,the localization accuracy is improved by 61.9%,which demonstrates good robustness and real-time performance.

关 键 词：SLAM 室内场景 全局平面地图 预提取 iPCA优化 平面约束 

分 类 号：TN249[电子电信—物理电子学]