顾及地物邻域特征的LiDAR点云布料模拟滤波优化
wf（2023）
Abstract
布料模拟滤波算法在LiDAR点云的地物分离中起到重要作用.但是,此方法得到的地面点云中往往含有残留的非地面点云,导致滤波分类结果不彻底、不准确.本文提出一种顾及地物邻域特征的布料模拟LiDAR点云滤波自动优化算法,可以快速精准地解决这一问题.首先,对LiDAR点云进行布料模拟滤波处理,在其分类结果的基础上对非地面点云进行组件分割,得到具有缓冲区域的一系列点云子集;其次,拟合子集所在地面点云得到主平面,对地面点云进行高程归一化处理;最后,利用第三势差算法对每块地面点云进行滤波优化处理.选取三组有代表性的实验数据进行测试,结果表明:相较于布料模拟滤波算法,本文方法整体精度得到提升,尤其是在布料模拟滤波算法的Ⅱ类误差上有着明显优化效果,Ⅱ类精度平均提升12.3％,可有效解决布料模拟滤波算法分类结果中存在残留的问题.
More求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined