Mapping Walls of Indoor Environment using RGB-D Sensor

TL;DR

This paper presents a real-time method for inferring indoor wall configurations using a moving RGB-D sensor, aiding robots in environment understanding without relying on Manhattan assumptions.

Contribution

The paper introduces a fast, online wall detection approach from RGB-D data that does not depend on Manhattan World assumptions, enabling better robot navigation.

Findings

Achieved accurate wall detection in real-time

Demonstrated effectiveness on MIT Stata Center Dataset

Method operates efficiently without Manhattan assumptions

Abstract

Inferring walls configuration of indoor environment could help robot "understand" the environment better. This allows the robot to execute a task that involves inter-room navigation, such as picking an object in the kitchen. In this paper, we present a method to inferring walls configuration from a moving RGB-D sensor. Our goal is to combine a simple wall configuration model and fast wall detection method in order to get a system that works online, is real-time, and does not need a Manhattan World assumption. We tested our preliminary work, i.e. wall detection and measurement from moving RGB-D sensor, with MIT Stata Center Dataset. The performance of our method is reported in terms of accuracy and speed of execution.