A Fast Multi-Scale of Distributed Batch-Learning Growing Neural Gas for Multi-Camera 3D Environmental Map Building

Biologically inspired intelligent methods have been applied to various sensing systems in order to extract features from a huge size of raw sensing data. For example, point cloud data can be applied to human activity recognition, multi-person tracking, and suspicious person detection, but a single R...

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Bibliographic Details
Published in:Biomimetics (Basel, Switzerland) Switzerland), 2024-09, Vol.9 (9), p.560
Main Authors: Siow, Chyan Zheng, Saputra, Azhar Aulia, Obo, Takenori, Kubota, Naoyuki
Format: Article
Language:English
Subjects:
Algebraic topology
Algorithms
Artificial intelligence
Biomimetics
Calibration
Cameras
Connectivity
Digital mapping
Electronic cameras
Engineering research
growing neural gas
Mapping
Methods
multiple-camera calibration
Neural networks
Older people
Robotics
Sensors
topological mapping
Topology
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Summary:Biologically inspired intelligent methods have been applied to various sensing systems in order to extract features from a huge size of raw sensing data. For example, point cloud data can be applied to human activity recognition, multi-person tracking, and suspicious person detection, but a single RGB-D camera is not enough to perform the above tasks. Therefore, this study propose a 3D environmental map-building method integrating point cloud data measured via multiple RGB-D cameras. First, a fast multi-scale of distributed batch-learning growing neural gas (Fast MS-DBL-GNG) is proposed as a topological feature extraction method in order to reduce computational costs because a single RGB-D camera may output 1 million data. Next, random sample consensus (RANSAC) is applied to integrate two sets of point cloud data using topological features. In order to show the effectiveness of the proposed method, Fast MS-DBL-GNG is applied to perform topological mapping from several point cloud data sets measured in different directions with some overlapping areas included in two images. The experimental results show that the proposed method can extract topological features enough to integrate point cloud data sets, and it runs 14 times faster than the previous GNG method with a 23% reduction in the quantization error. Finally, this paper discuss the advantage and disadvantage of the proposed method through numerical comparison with other methods, and explain future works to improve the proposed method.
ISSN:2313-7673
2313-7673
DOI:10.3390/biomimetics9090560