Meta-rooms: Building and maintaining long term spatial models in a dynamic world

TLDR

The authors propose a method to reconstruct the static layout of cluttered office environments—termed a meta‑room—from repeated RGB‑D observations by a robot, and to track dynamic objects across time. The approach iteratively processes point‑cluster data, detecting changes between successive RGB‑D scans to remove dynamic elements, recover occluded objects, and update the meta‑room, using the latest estimate to isolate dynamic clusters for subsequent re‑identification.

Abstract

We present a novel method for re-creating the static structure of cluttered office environments - which we define as the "meta-room" - from multiple observations collected by an autonomous robot equipped with an RGB-D depth camera over extended periods of time. Our method works directly with point clusters by identifying what has changed from one observation to the next, removing the dynamic elements and at the same time adding previously occluded objects to reconstruct the underlying static structure as accurately as possible. The process of constructing the meta-rooms is iterative and it is designed to incorporate new data as it becomes available, as well as to be robust to environment changes. The latest estimate of the meta-room is used to differentiate and extract clusters of dynamic objects from observations. In addition, we present a method for re-identifying the extracted dynamic objects across observations thus mapping their spatial behaviour over extended periods of time.