PLVO

RGB-D Odometry Using Point and Line Features

Introduction

Lighting variation and uneven feature distribution are main challenges for indoor RGB-D visual odometry where color information is often combined with depth information. To meet the challenges, we fuse point and line features to form a robust odometry algorithm. Line features are abundant indoors and less sensitive to lighting change than points. We extract 3D points and lines from RGB-D data, analyze their measurement uncertainties, and compute camera motion using maximum likelihood estimation. We prove that fusing points and lines produces smaller motion estimate uncertainty than using either feature type alone.

line detection

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Source code

Datasets

Publication

Yan Lu and Dezhen Song, "Robust RGB-D Odometry Using Point and Line Features", IEEE International Conference on Computer Vision (ICCV), 2015

Links

Netbot Lab

RGB-D Odometry Using Point and Line Features

Introduction
Lighting variation and uneven feature distribution are main challenges for indoor RGB-D visual odometry where color information is often combined with depth information. To meet the challenges, we fuse point and line features to form a robust odometry algorithm. Line features are abundant indoors and less sensitive to lighting change than points. We extract 3D points and lines from RGB-D data, analyze their measurement uncertainties, and compute camera motion using maximum likelihood estimation. We prove that fusing points and lines produces smaller motion estimate uncertainty than using either feature type alone.

Download
Source code Datasets
Publication
Yan Lu and Dezhen Song, "Robust RGB-D Odometry Using Point and Line Features", IEEE International Conference on Computer Vision (ICCV), 2015
Links
Netbot Lab