Long-term 3D map management is a fundamental capability required by a robot to reliably navigate in the non-stationary real-world. This paper develops open-source, modular, and readily available LiDAR-based lifelong mapping for urban sites. This is achieved by dividing the problem into successive subproblems: multi-session SLAM (MSS), high/low dynamic change detection, and positive/negative change management. The proposed method leverages MSS and handles potential trajectory error; thus, good initial alignment is not required for change detection. Our change management scheme preserves efficacy in both memory and computation costs, providing automatic object segregation from a large-scale point cloud map. We verify the framework's reliability and applicability even under permanent year-level variation, through extensive real-world experiments with multiple temporal gaps (from day to year).

翻译：长期 3D 地图管理是机器人可靠地在非静止现实世界中导航的基本能力。 本文开发了城市地点的开放源码、模块和随时可用的以LiDAR为基础的终身绘图。 实现这一点的方法是将问题分为一系列子问题:多部分SLM(MSS)、高/低动态变化探测和积极/消极变化管理。 拟议的方法利用MSS并处理潜在的轨迹错误; 因此,在发现变化时不需要良好的初始调整。 我们的变革管理计划既保存记忆成本,也保留计算成本的效率,从大型点云图中提供自动的物体隔离。 我们核查框架的可靠性和可适用性,即使在永久性的年度变化下,通过具有多种时间差距(从一天到一年)的广泛实际实验。