A Fast Multi-Scale of Distributed Batch-Learning Growing Neural Gas for Multi-Camera 3D Environmental Map Building
Chyan Zheng Siow, Azhar Aulia Saputra, Takenori Obo, Naoyuki Kubota
TL;DR
This paper introduces a fast method for building 3D maps using multiple cameras by improving neural gas algorithms and reducing computational costs.
Contribution
A novel Fast MS-DBL-GNG algorithm is proposed for efficient topological feature extraction from multi-camera point cloud data.
Findings
The proposed method runs 14 times faster than the previous GNG method.
It achieves a 23% reduction in quantization error.
The method effectively integrates point cloud data from multiple RGB-D cameras.
Abstract
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…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Medical Image Segmentation Techniques · Advanced Image and Video Retrieval Techniques