Indoor mapping using planes extracted from noisy RGB-D sensors

This paper presents a fast and robust plane feature extraction and matching technique for RGB-D type sensors. We propose three algorithm components required to utilize the plane features in an online Simultaneous Localization and Mapping (SLAM) problem: fast plane extraction, frame-to-frame constrai...

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Bibliographic Details
Main Authors: Tae-kyeong Lee, Seungwook Lim, Seongsoo Lee, Shounan An, Se-young Oh
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Estimation
Feature extraction
Image segmentation
Simultaneous localization and mapping
Vectors
Online Access:Request full text
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Summary:This paper presents a fast and robust plane feature extraction and matching technique for RGB-D type sensors. We propose three algorithm components required to utilize the plane features in an online Simultaneous Localization and Mapping (SLAM) problem: fast plane extraction, frame-to-frame constraint estimation, and plane merging. For the fast plane extraction, we estimate local surface normals and curvatures by a simple spherical model and then segment points using a modified flood fill algorithm. In plane parameter estimation, we suggest a new uncertainty estimation method which is robust against the measurement bias, and also introduce a fast boundary modeling method. We associate the plane features based on both the parameters and the spatial coverage, and estimate the stable constraints by the cost function with a regulation term. Also, our plane merging technique provides a way of generating local maps that are useful for estimating loop closure constraints. We have performed real-world experiments at our lab environment. The results demonstrate the efficiency and robustness of the proposed algorithm.
ISSN:2153-0858
2153-0866
DOI:10.1109/IROS.2012.6385909