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<!DOCTYPE HTML>
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不是每个人都应该像我这样去建造一座水晶大教堂，但是每个人都应该拥有自己的梦想，设计自己的梦想，追求自己的梦想，实现自己的梦想。梦想是生命的灵魂，是心灵的灯塔，是引导人走向成功的信仰。有了崇高的梦想，只要矢志不渝地追求，梦想就会成为现实，奋斗就会变成壮举，生命就会创造奇迹。——罗伯·舒乐
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<span class="card-title">即时到位与建图</span>
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<div class="summary block-with-text">
传感器
一类传感器是携带于机器人本体上的， 例如机器人的轮式编码器、 相机、 激光等等。 另 一类是安装于环境中的，例如前面讲的导轨、二维码标志等等。安装于环境中的传感设备， 通常能够直接测量到机器人的位置信息，简单有效地解决定位问题。然而
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<i class="far fa-clock fa-fw icon-date"></i>2022-01-04
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SLAM
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<span class="chip bg-color">SLAM十四讲</span>
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<span class="card-title">非线性优化</span>
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非线性优化
状态估计问题
最大后验与最大似然
经典slam模型：
{xk=f(xk−1,uk)+wkzk,j=h(yj,xk)+vk,j\begin{cases}
\boldsymbol{x}_k=f(\boldsymbol{x}_{k-
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SLAM
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<span class="card-title">SLAM</span>
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SLAM
SLAM 是 Simultaneous Localization and Mapping 的缩写，中文译作“同时定位与 地图构建”。它是指搭载特定传感器的主体，在没有环境先验信息的情况下，于运动过 程中建立环境的模型，同时估计自己
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<img src="/medias/featureimages/1.jpg" class="responsive-img" alt="位姿的数学描述">
<span class="card-title">位姿的数学描述</span>
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旋转矩阵
相机可以看成三维空间的刚体，于是位置是指相机在空间中的哪个地方，而姿态则是指相机的朝向。
内积可以描述向量间的投影关系。
外积表示向量的旋转。
a×b=[ijka1a2a3b1b2b3]=[a2b3−a3b2a3b1−a1b3a1
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相机模型
针孔模型和畸变构成了相机的内参数。
针孔相机模型
物体与像的数学模型
像与像素的数学模型
{u=αX′+cx=fxXZ+cxv=βY′+cy=fyYZ+cy\begin{cases}
u=\alpha X^{'}+c
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<span class="card-title">李群与李代数</span>
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李群与李代数
在 SLAM 中位姿是未知的，而我们需要解决什 么样的相机位姿最符合当前观测数据这样的问题。一种典型的方式是把它构建成一个优化 问题，求解最优的 R;t，使得误差最小化。 通过李群——李代数间的转换关系，旋转矩阵自身是带有约束
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提升方法
笔记摘要
在PAC（概率近似正确(PAC, Probably approximately correct)）学习框架下，一个概念是强可学习的充分必要条件是这个概念是弱可学习的。
提升方法的两个问题
在每一轮如何改变训练数据的
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支持向量机
笔记摘要
SVM的基本模型是定义在特征空间上的间隔最大的线性分类器
线性可分支持向量机和线性支持向量机假设输入空间和特征空间的元素一一对应，并将输入空间中的输入映射为特征空间的特征向量；非线性支持向量机利用一个从输入空间到特征
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逻辑斯谛回归与最大熵模型
logistic regression是统计学习中的经典分类方法。最大熵是概率模型学习的一个准则，推广到分类问题得到最大熵模型(maxium entropy model)
这两种模型都属于对数线性模型
逻辑斯谛
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决策树
笔记摘要
决策树可以认为是if-then规则的集合，也可以认为是定义在特征空间上的条件概率分布
根据损失函数最小化的原则建立决策树模型
决策树的路径或其对应的if-then规则集合具有一个重要性质：互斥且完备
决策树的学习算法包含
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