A JOINT EFFORT OF SPEEDED-UP ROBUST FEATURES ALGORITHM AND A DISPARITY-BASED MODEL FOR 3D INDOOR MAPPING USING RGB-D DATA

Marcos Aurélio Basso,Daniel Rodrigues Dos Santos

doi:10.1590/s1982-21702018000300023

Abstract

Abstract In this paper, we present a method for 3D mapping of indoor environments using RGB-D data. The contribution of our proposed method is two-fold. First, our method exploits a joint effort of the speed-up robust features (SURF) algorithm and a disparity-to-plane model for a coarse-to-fine registration procedure. Once the coarse-to-fine registration task accumulates errors, the same features can appear in two different locations of the map. This is known as the loop closure problem. Then, the variance-covariance matrix that describes the uncertainty of transformation parameters (3D rotation and 3D translation) for view-based loop closure detection followed by a graph-based optimization are proposed to achieve a 3D consistent indoor map. To demonstrate and evaluate the effectiveness of the proposed method, experimental datasets obtained in three indoor environments with different levels of details are used. The experimental results shown that the proposed framework can create 3D indoor maps with an error of 11,97 cm into object space that corresponds to a positional imprecision around 1,5% at the distance of 9 m travelled by sensor.

Highlights

Nowadays, RGB-D sensors are quite useful solution to build colored 3D indoor maps because it can exploit both the visual and the depth information
A sequence of handled data was captured in order to test the suitability of the devised method for 3D indoor mapping applications
A joint effort of the speed-up robust features algorithm and a disparity-toplane model for a coarse-to-fine registration procedure is exploited. This strategy avoids the solution of difficult convergence, once errors in RGB-D data association negatively affect the performance of the registration procedure

Summary

Introduction

RGB-D sensors are quite useful solution to build colored 3D indoor maps because it can exploit both the visual and the depth information. Its advantages compared with LASER scanning sensors are the lightweight, the low cost and it is much more flexible (Dos Santos et al, 2016). In this paper we propose a method for 3D indoor mapping using RGB-D data. The contribution of our proposed method is two-fold. We propose a joint effort of speed-up. M. and Santos, D.R. robust features (Bay et al, 2008) and a disparity-based model (Dos Santos et al, 2016) to include additional constraints in the graph describing the coarse-to-fine registration between RGB-D data. We investigate the variance-covariance matrix that describes the uncertainty of the transformation parameters (3D rotation and 3D translation) to weight the graph-based optimization

Objectives

Methods

Results

Conclusion