Chrome Extension
WeChat Mini Program
Use on ChatGLM
VIPCreated with Sketch.

    • 面向地下空间探测的移动机器人定位与感知方法

    BAI Shiyu,LAI Jizhou,Lü Pin,JI Bowen,ZHENG Xinyue,FANG Wei,CEN Yiting

    Jiqiren/Robot（2022）

    Cited 1|Views13
    Abstract
    提出了一种面向地下空间探测的移动机器人定位与感知方法.首先,针对地下空间的结构退化问题,构建了基于因子图的激光雷达/里程计/惯性测量单元紧耦合融合框架;推导了高精度惯性测量单元/里程计的预积分模型,利用因子图算法实现对移动机器人运动状态及传感器参数的同步估计.同时,提出了基于激光雷达/红外相机融合的目标识别方法,能够对弱光照环境下的多种目标进行识别与相对定位.试验结果表明,在结构退化环境中,本文方法能够将移动机器人的定位精度提升50％以上,并对弱光照环境中的目标实现厘米级的相对定位精度.
    More
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    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
    【要点】：本文提出了一种在地下空间探测中应用的移动机器人定位与感知方法，通过激光雷达、里程计和惯性测量单元的紧耦合融合，以及激光雷达与红外相机的融合，实现了高精度的定位和目标识别。
    

    【方法】：构建基于因子图的激光雷达/里程计/惯性测量单元紧耦合融合框架，并推导高精度惯性测量单元/里程计的预积分模型，同时提出激光雷达/红外相机融合的目标识别方法。
    

    【实验】：在地下结构退化环境中进行实验，使用的数据集未具体提及，实验结果表明定位精度提升50%以上，弱光照环境中目标相对定位精度达到厘米级。