A Novel Robust Scene Change Detection Algorithm for Autonomous Robots Using Mixtures of Gaussians


Interest in change detection techniques has considerably increased during recent years in the field of autonomous robotics. This is partly because changes in a robot's working environment are useful for several robotic skills (e.g., spatial cognition, modelling or navigation) and applications (e.g., surveillance or guidance robots). Changes are usually detected by comparing current data provided by the robot's sensors with a previously known map or model of the environment. When the data consists of a large point cloud, dealing with it is a computationally expensive task, mainly due to the amount of points and the redundancy. Using Gaussian Mixture Models (GMM) instead of raw point clouds leads to a more compact feature space that can be used to efficiently process the input data. This allows us to successfully segment the set of 3D points acquired by the sensor and reduce the computational load of the change detection algorithm. However, the segmentation of the environment as a Mixture of Gaussians has some problems that need to be properly addressed. In this paper, a novel change detection algorithm is described in order to improve the robustness and computational cost of previous approaches. The proposal is based on the classic Expectation Maximization (EM) algorithm, for which different selection criteria are evaluated. As demonstrated in the experimental results section, the proposed change detection algorithm achieves the detection of changes in the robot's working environment faster and more accurately than similar approaches.

Publication DOI: https://doi.org/10.5772/57360
Divisions: College of Engineering & Physical Sciences
Additional Information: This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publication ISSN: 1729-8814
Last Modified: 16 Apr 2024 07:31
Date Deposited: 08 Jun 2021 13:42
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Related URLs: https://journal ... i/10.5772/57360 (Publisher URL)
PURE Output Type: Article
Published Date: 2014-01-01
Authors: Manso, Luis J. (ORCID Profile 0000-0003-2616-1120)
Núñez, Pedro
Silva, Sidnei da
Drews-Jr, Paulo



Version: Published Version

License: Creative Commons Attribution

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