论文标题
大规模激光雷达一致地映射使用层次激光束调节
Large-Scale LiDAR Consistent Mapping using Hierachical LiDAR Bundle Adjustment
论文作者
论文摘要
重建准确且一致的大规模激光点云图映射对于机器人应用至关重要。现有的解决方案姿势图优化虽然及时，但并未直接优化映射一致性。 Lidar Bundle调整（BA）最近被提议解决此问题；但是，它在大型地图上太耗时了。为了减轻此问题，本文介绍了适合大规模地图的全球一致且有效的映射方法。我们提出的工作包括自下而上的分层BA和自上而下的姿势图优化，结合了两种方法的优势。通过层次设计，我们解决了比原始BA小得多的Hessian矩阵大小要小得多的BA问题；借助姿势图优化，我们可以平稳有效地更新LiDAR姿势。我们提出的方法的有效性和鲁棒性已在多个空间和及时的大规模公共旋转雷达数据集上进行了验证，即Kitti，Mulran和Newer College，以及在结构化和非结构化场景下进行自我收集的固态LIDAR数据集。通过适当的设置，我们证明我们的工作可以产生全球一致的地图，约有序列时间的12％。
Reconstructing an accurate and consistent large-scale LiDAR point cloud map is crucial for robotics applications. The existing solution, pose graph optimization, though it is time-efficient, does not directly optimize the mapping consistency. LiDAR bundle adjustment (BA) has been recently proposed to resolve this issue; however, it is too time-consuming on large-scale maps. To mitigate this problem, this paper presents a globally consistent and efficient mapping method suitable for large-scale maps. Our proposed work consists of a bottom-up hierarchical BA and a top-down pose graph optimization, which combines the advantages of both methods. With the hierarchical design, we solve multiple BA problems with a much smaller Hessian matrix size than the original BA; with the pose graph optimization, we smoothly and efficiently update the LiDAR poses. The effectiveness and robustness of our proposed approach have been validated on multiple spatially and timely large-scale public spinning LiDAR datasets, i.e., KITTI, MulRan and Newer College, and self-collected solid-state LiDAR datasets under structured and unstructured scenes. With proper setups, we demonstrate our work could generate a globally consistent map with around 12% of the sequence time.
