激光点云匹配是影响激光SLAM系统精度和效率的关键因素.传统激光SLAM算法无法区分场景结构,且在非结构化场景下由于特征提取不佳而出现性能退化.为此,提出一种联合CPD(coherent point drift)面向复杂场景的自适应激光SLAM算法CPD-LOAM.该算法提出一种基于预判和验证相结合的场景结构辨识方法,首先引入场景特征变量对场景结构进行初步判断,然后从几何特征角度通过表面曲率对其进行验证,增强对场景结构辨识的准确性.此外,在非结构化场景下添加CPD算法进行点云预配准,进而利用ICP算法进行再配准,解决该场景下的特征退化问题,从而提高点云配准的精度和效率.实验结果表明,提出的场景特征变量以及表面曲率可以根据设置的阈值有效地区分场景结构,在公开数据集KITTI上的验证结果显示,CPD-LOAM较LOAM算法定位误差降低了 84.47％,相较于LeGO-LOAM与LIO-SAM算法定位精度也分别提升了 55.88％和 30.52％,且具有更高的效率和鲁棒性.
Adaptive Laser SLAM Algorithm Combining CPD for Complex Scenes
Laser point cloud matching is a key factor affecting the accuracy and efficiency of laser SLAM systems.Traditional laser SLAM algorithms cannot effectively distinguish scene structures and result in performance degradation due to poor feature extraction in unstructured scenes.To address this issue,a joint coherent point drift(CPD)adaptive laser SLAM algorithm for complex scenes is proposed,called CPD-LOAM.First,a scene structure identification method combining prejudgment and verification is proposed,in which scene feature variables are introduced to make preliminary judgments on the scene structure.Then,surface curvature is further used to verify the preliminary judgments from the perspective of geometric features,enhancing the accuracy of scene structure identification.In addition,the CPD algorithm is combined for point cloud pre-registration in unstructured scenes,and then the ICP algorithm is used for re-registration to solve the problem of feature degradation in this scene,thereby improving the accuracy and efficiency of point cloud registration.The experimental results show that the proposed scene feature variables and surface curvature can effectively distinguish structure scenes based on the set threshold.The validation results on the public dataset KITTI show that CPD-LOAM reduces the positioning error by 84.47％compared to the LOAM algorithm,and improves the positioning accuracy by 55.88％and 30.52％respectively,compared to the LEGO-LOAM and LIO-SAM algorithms,with higher efficiency and robustness.
laser SLAMscene structurecoherent point drift(CPD)adaptive
万方数据
维普
