Predictive and Semantic Layout Estimation for Robotic Applications in Manhattan Worlds

TL;DR

This paper presents a method for robots to automatically generate complete floor plans from partial data by reasoning about structural layout surfaces, aiding navigation and planning in indoor environments.

Contribution

It introduces an online approach for extracting and reasoning about structural layout surfaces to create water-tight environment representations from incomplete measurements.

Findings

Effective in speculating room boundaries and free space regions.

Works on multiple data sets with promising results.

Supports online, real-time environment modeling.

Abstract

This paper describes an approach to automatically extracting floor plans from the kinds of incomplete measurements that could be acquired by an autonomous mobile robot. The approach proceeds by reasoning about extended structural layout surfaces which are automatically extracted from the available data. The scheme can be run in an online manner to build water tight representations of the environment. The system effectively speculates about room boundaries and free space regions which provides useful guidance to subsequent motion planning systems. Experimental results are presented on multiple data sets.