一种改进Cartographer算法的建图方法研究

Research on a method of improving cartographer algorithm

对于传统激光雷达建图算法中存在的点云特征提取不完善、质量低,噪声影响导致的位姿数据偏差影响建图效果的问题,提出了一种基于改进Cartographer算法的激光建图方法。首先,在传感器信息融合部分,用自适应无损卡尔曼滤波(AUKF)的方法,对传感器数据进行预测更新,再对噪声进行自适应优化,来减小噪声对位姿数据造成的影响;其次,在处理激光雷达采集的点云数据时,改进体素滤波的效果,通过对点云赋权过滤的方法进行点云信息的二次筛选,以降低点云的冗余,提升点云质量;最后,在真实环境进行建图测试,比较改进后算法与传统算法的建图效果,在室外环境下改进算法比原算法绝对平移误差降低了25.8%,绝对旋转误差降低了28.9%。可明显看出改进算法数据误差更小,建图效果更加精确。

Aiming at the problems of imperfect point cloud feature extraction and low quality in traditional laser radar mapping algorithm,and the deviation of pose data caused by noise affecting the mapping effect,this paper proposes a laser mapping method based on improved Cartographer algorithm.Firstly,in the sensor information fusion part,the Adaptive Lossless Kalman Filter(AUKF)method was used to predict and update the sensor data,and then the noise was adaptively optimized to reduce the influence of noise on the pose data.Secondly,when processing the point cloud data collected by lidar,the effect of voxel filtering was improved,and the secondary screening of point cloud information was carried out by the method of point cloud weighting filtering to reduce the redundancy of point cloud and improve the quality of point cloud.Finally,the mapping test was carried out in the real environment,and the mapping effect of the improved algorithm and the traditional algorithm was compared.In the outdoor environment,the absolute translation error of the improved algorithm was reduced by 25.8%and the absolute rotation error was reduced by 28.9%compared with the original algorithm.It can be clearly seen that the data error of the improved algorithm is smaller and the mapping effect is more accurate.