Automatic building exterior mapping using multilayer feature graphs

Abstract

We develop algorithms that can assist robot to perform building exterior mapping, which is important for building energy retrofitting. In this task, a robot needs to identify building facades in its localization and mapping process, which in turn can be used to assist robot navigation. Existing localization and mapping algorithms rely on low level features such as point clouds and line segments and cannot be directly applied to our task. We attack this problem by employing a multiple layer feature graph (MFG), which contains five different features ranging from raw key points to planes and vanishing points in 3D, in an extended Kalman filter (EKF) framework. We analyze how errors are generated and propagated in the MFG construction process, and then apply MFG data as observations for the EKF to map building facades. We have implemented and tested our MFG-EKF method at three different sites. Experimental results show that building facades are successfully constructed in modern urban environments with mean relative errors of plane depth less than 4.66%.