The contemporary SLAM mapping systems assume a static environment and build a map that is then used for mobile robot navigation disregarding the dynamic changes in this environment. The paper at hand presents a novel solution for the \emph{lifelong mapping} problem that continually updates a metric map represented as a 2D occupancy grid in large scale indoor environments with movable objects such as people, robots, objects etc. suitable for industrial applications. We formalize each cell's occupancy as a failure analysis problem and contribute temporal persistence modeling (TPM), an algorithm for probabilistic prediction of the time that a cell in an observed location is expected to be ``occupied" or ``empty" given sparse prior observations from a task specific mobile robot. Our work is evaluated in Gazebo simulation environment against the nominal occupancy of cells and the estimated obstacles persistence. We also show that robot navigation with lifelong mapping demands less re-plans and leads to more efficient navigation in highly dynamic environments.