LiDAR-based SLAM is a core technology for autonomous vehicles and robots. One key contribution of this work to 3D LiDAR SLAM and localization is a fierce defense of view-based maps (pose graphs with time-stamped sensor readings) as the fundamental representation of maps. As will be shown, they allow for the greatest flexibility, enabling the posterior generation of arbitrary metric maps optimized for particular tasks, e.g. obstacle avoidance, real-time localization. Moreover, this work introduces a new framework in which mapping pipelines can be defined without coding, defining the connections of a network of reusable blocks much like deep-learning networks are designed by connecting layers of standardized elements. We also introduce tightly-coupled estimation of linear and angular velocity vectors within the Iterative Closest Point (ICP)-like optimizer, leading to superior robustness against aggressive motion profiles without the need for an IMU. Extensive experimental validation reveals that the proposal compares well to, or improves, former state-of-the-art (SOTA) LiDAR odometry systems, while also successfully mapping some hard sequences where others diverge. A proposed self-adaptive configuration has been used, without parameter changes, for all 3D LiDAR datasets with sensors between 16 and 128 rings, and has been extensively tested on 83 sequences over more than 250~km of automotive, hand-held, airborne, and quadruped LiDAR datasets, both indoors and outdoors. The system flexibility is demonstrated with additional configurations for 2D LiDARs and for building 3D NDT-like maps. The framework is open-sourced online: https://github.com/MOLAorg/mola

翻译：基于激光雷达的SLAM是自动驾驶车辆与机器人的核心技术。本研究对三维激光雷达SLAM与定位领域的一项关键贡献在于，我们坚决主张以基于视图的地图（即带有时间戳传感器读数的位姿图）作为地图的基本表示形式。正如我们将要展示的，这种表示方式提供了最大的灵活性，支持后验生成针对特定任务（例如避障、实时定位）优化的任意度量地图。此外，本研究引入了一种新框架，使得无需编码即可定义建图流程，通过连接可复用模块网络来构建系统，其设计理念类似于通过连接标准化层来构建深度学习网络。我们还在类迭代最近点（ICP）优化器中引入了线性与角速度矢量的紧耦合估计，从而在无需惯性测量单元（IMU）的情况下，显著提升了对剧烈运动模式的鲁棒性。大量实验验证表明，该方案与先前最先进的激光雷达里程计系统相比具有同等或更优的性能，同时成功构建了其他方法会失效的若干困难序列的地图。所提出的自适应配置方案无需调整参数，即可适用于所有环数在16至128之间的三维激光雷达数据集，并在超过250公里的汽车载、手持式、机载以及四足机器人激光雷达数据集（包含室内外环境）的83个序列上进行了广泛测试。通过为二维激光雷达及构建类三维正态分布变换（NDT）地图提供的额外配置，进一步证明了该系统的灵活性。本框架已在线上开源：https://github.com/MOLAorg/mola