  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).
