论文标题
广义壤土：具有可训练的局部几何特征的激光镜射量估计
Generalized LOAM: LiDAR Odometry Estimation with Trainable Local Geometric Features
论文作者
论文摘要
本文提出了一种称为广义壤土的激光射击估计估计框架。我们提出的方法是普遍的，因为它可以无缝地融合各个局部几何形状，以提高与常规的激光镜射仪和映射（BOAM）方法相比的位置估计精度。为了利用连续的几何特征进行激光射击估计，我们将微小的神经网络纳入了广义的迭代最接近点（GICP）算法中。这些神经网络使用局部几何特征改善了数据关联度量和匹配成本函数。使用KITTI基准测试的实验表明，与其他激光射击估计方法相比，我们提出的方法减少了相对轨迹误差。
This paper presents a LiDAR odometry estimation framework called Generalized LOAM. Our proposed method is generalized in that it can seamlessly fuse various local geometric shapes around points to improve the position estimation accuracy compared to the conventional LiDAR odometry and mapping (LOAM) method. To utilize continuous geometric features for LiDAR odometry estimation, we incorporate tiny neural networks into a generalized iterative closest point (GICP) algorithm. These neural networks improve the data association metric and the matching cost function using local geometric features. Experiments with the KITTI benchmark demonstrate that our proposed method reduces relative trajectory errors compared to the other LiDAR odometry estimation methods.
