3D Mapping of Multi-floor Buildings Based on Sensor Fusion 论文的概括
时间: 2023-03-14 21:20:45 浏览: 97
3D Mapping of Multi-floor Buildings Based on Sensor Fusion 论文概括了一种基于传感器融合的多层楼宇三维建模方法。它采用了多种传感器,包括激光雷达、深度摄像机、IMU和GPS,利用这些传感器的特性,利用融合算法把多层楼宇的三维地图信息实时地重建出来。
相关问题
Quasi-Polynomial Mapping-based Root-Finder
A quasi-polynomial mapping-based root-finder is a method used in numerical analysis to find the roots of a quasi-polynomial function. Quasi-polynomials are functions that have periodic coefficients, and they can be used to model a wide range of phenomena in physics, engineering, and finance.
The basic idea behind the quasi-polynomial mapping-based root-finder is to transform the quasi-polynomial function into a polynomial function by mapping the coefficients onto the complex plane. This mapping allows us to apply existing polynomial root-finding algorithms, such as the Newton-Raphson method, to the transformed polynomial function.
The key advantage of this method is that it can handle quasi-polynomials with very high degrees and large periods, which would be difficult or impossible to solve using other methods. However, the mapping process can be computationally expensive, and the resulting polynomial function may have many roots, some of which are not relevant to the original quasi-polynomial function.
Overall, the quasi-polynomial mapping-based root-finder is a powerful tool for solving complex quasi-polynomial functions, but it requires careful implementation and analysis to ensure accurate and efficient results.
active bayesian multi-class mapping from range and semantic segmentation obs
主动贝叶斯多类映射是一种利用距离信息和语义分割观测进行地图构建的方法。在这个方法中,我们将地图划分为多个离散的类别,并使用激光雷达等传感器获取的距离信息和语义分割图像作为输入。
在这个方法中,我们首先使用激光雷达等传感器获取环境的距离信息。然后,我们将距离信息和语义分割图像结合起来,得到一个综合的观测结果。这个综合的观测结果包含了地图上每个像素点所属的类别信息以及该像素点与激光雷达的距离。
接下来,我们应用主动贝叶斯方法来进行多类映射。首先,我们定义每个类别的先验概率。这些先验概率可以通过建立训练集并对其进行统计分析得到。然后,我们利用贝叶斯定理来计算给定观测结果下每个类别的后验概率。通过比较后验概率,我们可以确定每个像素点所属的类别。
在多类映射过程中,我们可以使用主动探索策略来提高地图构建的效果。主动探索策略可以根据当前地图的不确定度来选择下一个最有信息量的观测位置。通过这种方式,我们可以在有限的观测次数内尽可能准确地构建地图。
总结起来,主动贝叶斯多类映射方法利用距离信息和语义分割观测进行地图构建。它通过先验概率和后验概率的计算来确定每个像素点所属的类别,并利用主动探索策略来提高地图构建的效果。这种方法可以应用于自动驾驶、机器人导航等领域,为智能系统提供准确的环境感知和地图信息。
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