Siow Chyan Zheng, Saputra Azhar Aulia, Obo Takenori, Kubota Naoyuki
Graduate School of Systems Design, Tokyo Metropolitan University, Hino-shi 191-0065, Tokyo, Japan.
Biomimetics (Basel). 2024 Sep 16;9(9):560. doi: 10.3390/biomimetics9090560.
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.
受生物启发的智能方法已应用于各种传感系统，以便从海量原始传感数据中提取特征。例如，点云数据可应用于人类活动识别、多人跟踪和可疑人员检测，但单个RGB-D相机不足以执行上述任务。因此，本研究提出了一种整合通过多个RGB-D相机测量的点云数据的3D环境地图构建方法。首先，提出了一种快速多尺度分布式批学习生长神经气体（Fast MS-DBL-GNG）作为拓扑特征提取方法，以降低计算成本，因为单个RGB-D相机可能输出100万个数据。接下来，应用随机抽样一致性（RANSAC）使用拓扑特征整合两组点云数据。为了展示所提方法的有效性，将Fast MS-DBL-GNG应用于从在不同方向测量的几个点云数据集进行拓扑映射，其中两个图像包含一些重叠区域。实验结果表明，所提方法能够提取足够的拓扑特征以整合点云数据集，并且其运行速度比先前的GNG方法快14倍，量化误差降低了23%。最后，本文通过与其他方法的数值比较讨论了所提方法的优缺点，并阐述了改进所提方法的未来工作。
