.png){kind=icon}
返回
Adaptive and Efficient Mixture-Based Representation for Range Data
.png){kind=icon}
OA
AI
DOI：10.3390/s20113272.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Modern range sensors generate millions of data points per second, making it difficult to utilize all incoming data effectively in real time for devices with limited computational resources. The Gaussian mixture model (GMM) is a convenient and essential tool commonly used in many research domains. In this paper, an environment representation approach based on the hierarchical GMM structure is proposed, which can be utilized to model environments with weighted Gaussians. The hierarchical structure accelerates training by recursively segmenting local environments into smaller clusters. By adopting the information-theoretic distance and shape of probabilistic distributions, weighted Gaussians can be dynamically allocated to local environments in an arbitrary scale, leading to a full adaptivity in the number of Gaussians. Evaluations are carried out in terms of time efficiency, reconstruction, and fidelity using datasets collected from different sensors. The results demonstrate that the proposed approach is superior with respect to time efficiency while maintaining the high fidelity as compared to other state-of-the-art approaches.
Keyword:
gaussian mixture model
environment representation
hierarchical structure
point cloud data
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
The Toll→NFκB Signaling Pathway Mediates the Neuropathological Effects of the Human Alzheimer's Aβ42 Polypeptide in Drosophila.png)
PLoS ONEOAAI
PLoS ONEIF0
OAAI分享收藏A Novel GMM-Based Behavioral Modeling Approach for Smartwatch-Based Driver AuthenticationSENSORSOAAI
SENSORSIF3.5
OAAI分享收藏分享收藏OctoMap: an efficient probabilistic 3D mapping framework based on octreesOctoMap: 一种基于八叉树的高效概率三维映射框架AUTONOMOUS ROBOTSOAAI
AUTONOMOUS ROBOTSIF4.3
OAAI分享收藏Building Extraction From LiDAR Data Applying Deep Convolutional Neural Networks应用深度卷积神经网络从LiDAR数据中提取建筑物
PREAI
分享收藏
查看更多内容