IMU一般输出至少有六轴传感器信息,包括三轴的角速度(陀螺仪)以及三轴的线加速度(加速度计)。值得注意的是IMU的测量值依赖于惯性系,这个是由其测量模型决定的,同时IMU一般刚性安装在机器人(小车)上,且所以我们一般认为IMU输出的是机体坐标系下的惯性测量值。
测量模型
加速度计
首先对...
NDT算法的大名为Normal Distribution Transform,即正态分布变换,用于激光点云配准。算法的核心也正如其名,将目标点云按照体素(栅格)的方法划分,得到每个格子中点云的正态分布参数,再计算待配准点云在各个格子中的概率分布之和,这个值越大,那么配准结果越好。
在前文的讨论中,我们从线性回归出发,一步步延展到全连接的多层网络,也使用了一个多层网络去进行图像处理。但是值得注意的是,事实上,我们是仅仅将这些图像数据展平成了一维的向量,很显然这忽略了图像的空间特征【我们都知道图像的数据存储模式是一个多维的矩阵,嗯,是的都知道的】;同时对于一个百万像素的图像...
在机器学习领域,仿射变换(带偏置的线性变换)是基础模型架构的核心,如 softmax 回归通过单一线性映射加激活函数实现分类。但线性假设存在显著局限:
单调性约束过强:例如收入与还款概率虽呈正相关,但非严格线性;体温与死亡率更呈现非线性的 “U 型” 关系,简单线性模型无法准确建模。
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线性回归
线性回归(linear regression)使用一个线性模型来定义, 将所有特征放到向量$\mathbf{x} \in \mathbb{R}^d $ 中,并将所有权重放到向量 ,$\mathbf{w} \in \mathbb{R}^d$ 中,我们可以用点积形式来简洁地表达模型:
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针孔相机
VSLAM前端的核心目的是得到相机的位姿状态与环境信息,特征点法则是现有体系中VSLAM前端的主要方法之一,能够通过特征点在图像中位置的变化来得到位姿。其基本原理可以概述为,空间中一个固定的点在不同位置的相机中观测到的是不同的,那么我们可以根据其中的几何关系来推知不同位置的相机之间...
The role of unconstrained optimization in robotics is unquestionable. This blog simply records the derivation of the Gauss-Newton method in unconst...
The Taylor expansion of a function expands a nonlinear function at a certain point to obtain a polynomial that can approximate the original functio...
For high-dimensional Gaussian distribution $X\sim N(\mu,\Sigma)$,
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A vector space is a special set formed by a group of mathematical fields ...
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