# 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.

## Key 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.