Abstract-Many robotics tasks require the robot to predict what lies in the unexplored part of the environment. Although much work focuses on building autonomous robots that operate indoors, indoor environments are neither well understood nor analyzed enough in the literature. In this paper, we propose and compare two methods for predicting both the topology and the categories of rooms given a partial map. The methods are motivated by the analysis of two large annotated floor plan data sets corresponding to the buildings of the MIT and KTH campuses. In particular, utilizing graph theory, we discover that local complexity remains unchanged for growing global complexity in real-world indoor environments, a property which we exploit. In total, we analyze 197 buildings, 940 floors and over 38,000 real-world rooms. Such a large set of indoor places has not been investigated before in the previous work. We provide extensive experimental results and show the degree of transferability of spatial knowledge between two geographically distinct locations. We also contribute the KTH data set and the software tools to with it.
al. REF investigated the statistical properties of indoor environments by analyzing two large floor plan datasets consisting of over 197 buildings and 38,000 rooms in total.
What can we learn from 38,000 rooms? Reasoning about unexplored space in indoor environments
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
Computer Science