LiDAR-based SLAM is a core technology for autonomous vehicles and robots. Despite the intense research activity in this field, each proposed system uses a particular sensor post-processing pipeline and a single map representation format. The present work aims at introducing a revolutionary point of view for 3D LiDAR SLAM and localization: (1) using view-based maps as the fundamental representation of maps ("simple-maps"), which can then be used to generate arbitrary metric maps optimized for particular tasks; and (2) by introducing 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. Moreover, the idea of including the current linear and angular velocity vectors as variables to be optimized within the ICP loop is also introduced, leading to superior robustness against aggressive motion profiles without an IMU. The presented open-source ecosystem, released to ROS 2, includes tools and prebuilt pipelines covering all the way from data acquisition to map editing and visualization, real-time localization, loop-closure detection, or map georeferencing from consumer-grade GNSS receivers. 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, extensively tested on 83 sequences over more than 250~km of automotive, hand-held, airborne, and quadruped LiDAR datasets, both indoors and outdoors. The open-sourced implementation is available online at 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。尽管该领域研究活跃，但现有系统均采用特定的传感器后处理流程和单一的地图表示格式。本研究旨在为三维激光雷达SLAM与定位引入革命性视角：（1）采用基于视图的地图作为基础地图表示（"简单地图"），可据此生成针对特定任务优化的任意度量地图；（2）通过提出新型框架，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。此外，本文还创新性地将当前线速度与角速度向量作为ICP迭代优化变量，在不依赖IMU的情况下显著提升系统对剧烈运动模式的鲁棒性。所发布的开源生态系统已集成至ROS 2平台，包含从数据采集到地图编辑与可视化、实时定位、闭环检测及消费级GNSS接收器地图地理配准的全流程工具与预置管线。大量实验验证表明，该方案在性能上媲美或超越了现有最先进的激光雷达里程计系统，并成功完成了其他系统易失效的困难场景建图。所提出的自适应配置方案无需参数调整，即可适用于16至128线各类三维激光雷达数据集，在超过250公里的车载、手持、机载及四足机器人激光雷达数据（涵盖83个室内外序列）中通过全面测试。开源实现详见https://github.com/MOLAorg/mola。