This article presents a keyframe-based, innovative map registration scheme for applications that benefit from recurring data acquisition, such as long-term natural environment monitoring. The proposed method consists of a multi-stage pipeline, in which semantic knowledge of the scene is acquired using a pretrained neural network. The semantic knowledge is subsequently employed to constrain the Iterative Closest Point algorithm (ICP). In this article, semantic-aware ICP is used to build keyframes as well as to align them both spatially and temporally, with neighboring keyframes and those captured around the same area but at a different point in time, respectively. Hierarchical clustering of ICP-generated transformations is then used to both eliminate outliers and find alignment consensus, followed by an optimization scheme based on a factor graph that includes loop closure. To evaluate the proposed framework, data was captured using a portable robotic sensor suite consisting of three cameras, a three dimensional lidar, and an inertial navigation system. The data is acquired monthly over 12 months, by revisiting the same trajectory between August 2020 and July 2021.