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fa-envelope"></i></a></div></div><div id="scroll-down"><i class="fas fa-angle-down scroll-down-effects"></i></div></header><main class="layout" id="content-inner"><div class="recent-posts" id="recent-posts"><div class="recent-post-item"><div class="post_cover left_radius"><a href="/blog/2022/11/18/2022-11-18-%E8%A5%BF%E7%93%9C%E4%B9%A6%E5%AD%A6%E4%B9%A0/" title="西瓜书学习"> <img class="post_bg" src="https://s2.loli.net/2022/05/11/UxGlPip6knFAHdo.jpg" onerror="this.onerror=null;this.src='/blog/img/404.jpg'" alt="西瓜书学习"></a></div><div class="recent-post-info"><a class="article-title" href="/blog/2022/11/18/2022-11-18-%E8%A5%BF%E7%93%9C%E4%B9%A6%E5%AD%A6%E4%B9%A0/" title="西瓜书学习">西瓜书学习</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2022-11-18T04:30:12.000Z" title="发表于 2022-11-18 12:30:12">2022-11-18</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/blog/categories/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/">机器学习</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/blog/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/">机器学习</a></span></div><div class="content">参考网址第3章-一元线性回归_哔哩哔哩_bilibili
一元线性回归1234算法原理线性回归与极大似然估计求解w与b凸函数优化(优化角度定义)
12线性回归损失函数: E(w, b) = Σ(yi - f(xi))^2极大似然估计: L(theta) = ⫪P(xi; theta)
例题123样本符合正态分布 X ~ N(u, sigma^2)总体思路是,先假设为一个一元线性回归, y = wx + b + sigma,然后由于符合正态分布,转化为对数函数,然后求换成一下形式
机器学习三要素123模型: 根据具体问题, 确定假设空间策略: 根据评价标准, 确定最优策略(通常会产生一个损失函数)算法: 求解损失函数, 确定最优模型
多元线性回归12由最小二乘求解损失函数Ew求解w
对数几率123算法原理损失函数极大似然估计推导损失函数信息论推导
信息论概念相对熵(描述两个分布的差异)1最小化相对熵的方式达到最接近模拟分布, 最小化型对上等价于最小化交叉熵
决策树1234算法原理ID3决策树C4.5决策树CART决策树
原理123从逻辑角度一堆的if else语句组 ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/blog/2022/11/11/2022-11-11-2D%E6%BF%80%E5%85%89slam%E6%95%B0%E6%8D%AE%E9%9B%86%E7%9A%84%E4%B8%8B%E8%BD%BD%E4%B8%8E%E6%B5%8B%E8%AF%95/" title="2D激光slam数据集的下载与测试"> <img class="post_bg" src="https://s2.loli.net/2022/05/11/vFfg9Vrwmy7uPbq.jpg" onerror="this.onerror=null;this.src='/blog/img/404.jpg'" alt="2D激光slam数据集的下载与测试"></a></div><div class="recent-post-info"><a class="article-title" href="/blog/2022/11/11/2022-11-11-2D%E6%BF%80%E5%85%89slam%E6%95%B0%E6%8D%AE%E9%9B%86%E7%9A%84%E4%B8%8B%E8%BD%BD%E4%B8%8E%E6%B5%8B%E8%AF%95/" title="2D激光slam数据集的下载与测试">2D激光slam数据集的下载与测试</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2022-11-11T04:30:12.000Z" title="发表于 2022-11-11 12:30:12">2022-11-11</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/blog/categories/slam/">slam</a><i class="fas fa-angle-right"></i><a class="article-meta__categories" href="/blog/categories/slam/%E5%B8%B8%E7%94%A8%E5%B7%A5%E5%85%B7/">常用工具</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/blog/tags/slam%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/">slam学习笔记</a></span></div><div class="content">参考网址(144条消息) 2D激光slam数据集的下载与测试_快乐飞奔的小菜鸡的博客-CSDN博客_2d slam数据集
(144条消息) 二维激光雷达SLAM数据集_哈哈哈的嘎嘎嘎的博客-CSDN博客_二维激光雷达
代码1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586# convert.py#!/usr/bin/env python#coding=utf8'''This is a converter for the Intel Research Lab SLAM dataset ( http://kaspar.informatik.uni-freiburg.de/~slamEvaluation/datasets/intel.clf ) to rosbag' ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/blog/2022/11/11/%E5%9B%A0%E5%AD%90%E5%9B%BE%E4%BC%98%E5%8C%96/" title="因子图优化"> <img class="post_bg" src="https://s2.loli.net/2022/05/11/ulqbCaZsiXkyjpY.jpg" onerror="this.onerror=null;this.src='/blog/img/404.jpg'" alt="因子图优化"></a></div><div class="recent-post-info"><a class="article-title" href="/blog/2022/11/11/%E5%9B%A0%E5%AD%90%E5%9B%BE%E4%BC%98%E5%8C%96/" title="因子图优化">因子图优化</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2022-11-11T04:30:12.000Z" title="发表于 2022-11-11 12:30:12">2022-11-11</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/blog/categories/slam/">slam</a><i class="fas fa-angle-right"></i><a class="article-meta__categories" href="/blog/categories/slam/%E7%90%86%E8%AE%BA%E5%AD%A6%E4%B9%A0/">理论学习</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/blog/tags/slam/">slam</a></span></div><div class="content">参考网址 【泡泡机器人公开课】第五十六课：gtsam_tutorial-董靖_哔哩哔哩_bilibili
T5 - 图优化 (第一节课)_哔哩哔哩_bilibili
战疫时期的算法课 - 知乎 (zhihu.com)
(164条消息) 【GTSAM】GTSAM/iSAM1/2资源整理_guoqiang_sunshine的博客-CSDN博客_gtsam isam
dongjing3309/minisam: A general and flexible factor graph non-linear least square optimization framework (github.com)
写一个简单的因子图库peitianyu/factor_graph (github.com)
简介123456此程序主要学习了mini_sam思路,讲真mini_sam库真心清晰,明了,干净.程序包含Core: 主函数在graph_optimize进行优化,然后通过sparsity_pattern保存稀疏矩阵缓存,variable与factor构造虚函数types: 构造二维空间下的variabl ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/blog/2022/11/07/2022-11-7-ransac%E5%AD%A6%E4%B9%A0/" title="ransac学习"> <img class="post_bg" src="https://s2.loli.net/2022/05/11/KGx8sIvuFt3N9P7.jpg" onerror="this.onerror=null;this.src='/blog/img/404.jpg'" alt="ransac学习"></a></div><div class="recent-post-info"><a class="article-title" href="/blog/2022/11/07/2022-11-7-ransac%E5%AD%A6%E4%B9%A0/" title="ransac学习">ransac学习</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2022-11-07T04:30:12.000Z" title="发表于 2022-11-07 12:30:12">2022-11-07</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/blog/categories/slam/">slam</a><i class="fas fa-angle-right"></i><a class="article-meta__categories" href="/blog/categories/slam/%E7%90%86%E8%AE%BA%E5%AD%A6%E4%B9%A0/">理论学习</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/blog/tags/slam%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/">slam学习笔记</a></span></div><div class="content">Ransac123流程:在所有point中选择一组计算line参数,通过设置k_min与k_max保留局内点,下一轮中重新选点,若得分更高则认为该模型更好,如此循环直到达到设置阈值或者最大迭代
1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162//RANSAC 拟合2D 直线//输入参数：points--输入点集// iterations--迭代次数// sigma--数据和模型之间可接受的差值,车道线像素宽带一般为10左右// （Parameter use to compute the fitting score）// k_min/k_max--拟合的直线斜率的取值范围.// 考虑到左右车道线在图像中的斜率位于一定范围内，// 添加此参数，同时可以避免检测垂 ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/blog/2022/11/04/2022-11-4-%E6%AF%8F%E6%97%A5%E5%AD%A6%E4%B9%A0%E9%97%AE%E7%AD%94%E7%AC%94%E8%AE%B0/" title="每日学习问答笔记"> <img class="post_bg" src="https://s2.loli.net/2022/05/11/QjKG5eupZdBSP82.jpg" onerror="this.onerror=null;this.src='/blog/img/404.jpg'" alt="每日学习问答笔记"></a></div><div class="recent-post-info"><a class="article-title" href="/blog/2022/11/04/2022-11-4-%E6%AF%8F%E6%97%A5%E5%AD%A6%E4%B9%A0%E9%97%AE%E7%AD%94%E7%AC%94%E8%AE%B0/" title="每日学习问答笔记">每日学习问答笔记</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2022-11-04T04:30:12.000Z" title="发表于 2022-11-04 12:30:12">2022-11-04</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/blog/categories/slam/">slam</a><i class="fas fa-angle-right"></i><a class="article-meta__categories" href="/blog/categories/slam/%E7%90%86%E8%AE%BA%E5%AD%A6%E4%B9%A0/">理论学习</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/blog/tags/slam%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/">slam学习笔记</a></span></div><div class="content">每日学习笔记222-11-312Q: 关于slam重定位的讨论A: 使用粒子滤波时通过增加历史权重的方式获得一定scan的历史信息,当信息量达到一定程度时,开始做匹配重定位,最后留下一个pose作为最终定位pose,但实际上这种通过增加历史权重的方式获得历史信息的方式依旧会损失很多scan信息,所以可以通过scan叠加图后通过叠成更大图的方式最后进行匹配,尽可能保留scan数据
1234567问题起因: 当厂家给我们传感器误差时的意思实际上就是说他们的传感器可以近似拟合高斯分布Q1: 为什么可以拟合成高斯分布?A1: 因为没有办法,只有高斯分布可以在传播过程中依然保持高斯分布,这种特性是其他分布所没有的,只能如此,所以这也就导致了这样一个问题,我们虽然拟合成高斯分布,但实际上传感器误差不可能仅仅遵循高斯分布,因此可以短期相信高斯分布的结果,但不可能完全相信它Q2: 考虑amcl中观测模型求解权重的情况,为什么其似然域模型可以包含随机误差与最大误差的影响A2: 因为这里的权重模型仅考虑单次本轮内比较,并不存在与下一轮或者上一轮比较的情况,因此可以用Q3: 为什么似然域模型需要将p *= ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/blog/2022/10/18/2022-10-18-%E5%93%88%E5%B7%A5%E5%A4%A7%E5%8E%9F%E8%88%AA%E6%A8%A1%E5%8D%8F%E4%BC%9A%E6%8A%80%E6%9C%AF%E6%80%BB%E7%9B%91%E6%95%99%E4%BD%A0%E6%80%BB%E4%BD%93%E8%AE%BE%E8%AE%A1%203-1/" title="哈工大原航模协会技术总监教你总体设计 3-1"> <img class="post_bg" src="https://s2.loli.net/2022/05/11/SjCm5qlzeYVvxUH.jpg" onerror="this.onerror=null;this.src='/blog/img/404.jpg'" alt="哈工大原航模协会技术总监教你总体设计 3-1"></a></div><div class="recent-post-info"><a class="article-title" href="/blog/2022/10/18/2022-10-18-%E5%93%88%E5%B7%A5%E5%A4%A7%E5%8E%9F%E8%88%AA%E6%A8%A1%E5%8D%8F%E4%BC%9A%E6%8A%80%E6%9C%AF%E6%80%BB%E7%9B%91%E6%95%99%E4%BD%A0%E6%80%BB%E4%BD%93%E8%AE%BE%E8%AE%A1%203-1/" title="哈工大原航模协会技术总监教你总体设计 3-1">哈工大原航模协会技术总监教你总体设计 3-1</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2022-10-18T04:30:12.000Z" title="发表于 2022-10-18 12:30:12">2022-10-18</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/blog/categories/%E9%A3%9E%E6%9C%BA/">飞机</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/blog/tags/%E9%A3%9E%E6%9C%BA/">飞机</a></span></div><div class="content">参考网址【硬核航模教程】02哈工大原航模协会技术总监教你总体设计3-1_哔哩哔哩_bilibili
总体设计流程123input -> 需求分析 -> 参数设计 -> 布局设计 -> 动力设计 -> 翼型选择 -> 重量估计 -> 操纵性能验证 -> output ^ | |_____________________________________________________|
需求分析12对场景分析,提炼功能与信息指标飞行场景分为: 起飞, 爬升, 巡航, 任务, 下降, 降落, 存放和运输 7个阶段
起飞1234起飞重量起飞距离起飞功率横风能力(稳定性能)
爬升12近地面爬升率升限(爬升率还剩余0.5m/s时飞行高度)
巡航过程123456航程航时巡航速度巡航高度环境适应性最小失速速度与最大失速迎角(安全飞行范围)
任务阶段12345678910111213141516稳定性要求: 纵向短周期模态 纵向长周期模态(一般不提) 荷兰滚模态 滚转模态 螺旋模态(一般不提 ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/blog/2022/10/18/2022-10-18-%E8%88%AA%E6%A8%A1%E8%AE%BE%E8%AE%A1%E5%88%B6%E4%BD%9C%E5%85%A5%E9%97%A8/" title="航模设计制作入门"> <img class="post_bg" src="https://s2.loli.net/2022/05/11/dF1hr2kwSmxQNWM.jpg" onerror="this.onerror=null;this.src='/blog/img/404.jpg'" alt="航模设计制作入门"></a></div><div class="recent-post-info"><a class="article-title" href="/blog/2022/10/18/2022-10-18-%E8%88%AA%E6%A8%A1%E8%AE%BE%E8%AE%A1%E5%88%B6%E4%BD%9C%E5%85%A5%E9%97%A8/" title="航模设计制作入门">航模设计制作入门</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2022-10-18T04:30:12.000Z" title="发表于 2022-10-18 12:30:12">2022-10-18</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/blog/categories/%E9%A3%9E%E6%9C%BA/">飞机</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/blog/tags/%E9%A3%9E%E6%9C%BA/">飞机</a></span></div><div class="content">参考网址航模设计制作入门_哔哩哔哩_bilibili
总体设计确定参数123451. 起飞总重2. 最大升力系数3. 零升阻力系数 : 预估飞机阻力4. 推重比 T/W5. 翼载荷 W/S
起飞总重1空载重量 + 任务载重
最大升力系数1确定最大能够给飞机提供的升力
零升阻力系数 : 预估飞机阻力1231. 摩擦 材料外形2. 压差 材料外形3. 诱导阻力 翼梢形成下洗气流 (大展弦比, 翼梢小翼)
推重比1234561. 起飞滑跑距离2. 根据平飞状态3. 根据爬升性能4. 根据最大平飞速度综合考虑后选择最大得推重比
翼载荷123456789101112131415影响时速速度, 爬升率, 起飞着陆距离, 盘旋性能, 决定了设计升力系数翼载对确定飞机起飞总重有很大影响,翼载小机翼大,能改善性能但机翼大同时会导致阻力与空机重量增大,导致起飞总重增大这里注意参考实战飞机:通用航空单发: 83 g/dm2 通用航空双发: 127g/dm2航模一般 100g/dm2比较好飞1. 根据失速速度2. 根据起飞距离计算3. 根据航程计算4. ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/blog/2022/10/12/chow_liu%20tree/" title="Chow_Liu Tree"> <img class="post_bg" src="https://s2.loli.net/2022/05/11/LxeFRMkuP2OagbD.jpg" onerror="this.onerror=null;this.src='/blog/img/404.jpg'" alt="Chow_Liu Tree"></a></div><div class="recent-post-info"><a class="article-title" href="/blog/2022/10/12/chow_liu%20tree/" title="Chow_Liu Tree">Chow_Liu Tree</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2022-10-12T04:30:12.000Z" title="发表于 2022-10-12 12:30:12">2022-10-12</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/blog/categories/slam/">slam</a><i class="fas fa-angle-right"></i><a class="article-meta__categories" href="/blog/categories/slam/%E7%90%86%E8%AE%BA%E5%AD%A6%E4%B9%A0/">理论学习</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/blog/tags/slam/">slam</a></span></div><div class="content">参考网址:(170条消息) SLAM中的位姿图稀疏化_qq_35201208的博客-CSDN博客
(170条消息) FAB_MAP算法的理论_宅男不宅的博客-CSDN博客_fabmap
毕业论文整理（二）：用依赖树近似离散概率分布 | 文章 | BEWINDOWEB
高仙IROS论文分享丨适用于变化环境下的长期建图定位系统 - 知乎 (zhihu.com)
Sci-Hub | Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, 14(3), 462–467 | 10.1109/TIT.1968.1054142
周刘树理解1二阶分布的乘积近似原联合分布,使用最大互信息,求得依赖树
</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/blog/2022/09/21/2022-9-21-%E5%9D%90%E6%A0%87%E5%8F%98%E6%8D%A2/" title="1. 坐标变换"> <img class="post_bg" src="https://s2.loli.net/2022/05/11/UxGlPip6knFAHdo.jpg" onerror="this.onerror=null;this.src='/blog/img/404.jpg'" alt="1. 坐标变换"></a></div><div class="recent-post-info"><a class="article-title" href="/blog/2022/09/21/2022-9-21-%E5%9D%90%E6%A0%87%E5%8F%98%E6%8D%A2/" title="1. 坐标变换">1. 坐标变换</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2022-09-21T04:30:12.000Z" title="发表于 2022-09-21 12:30:12">2022-09-21</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/blog/categories/slam/">slam</a><i class="fas fa-angle-right"></i><a class="article-meta__categories" href="/blog/categories/slam/%E7%90%86%E8%AE%BA%E5%AD%A6%E4%B9%A0/">理论学习</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/blog/tags/2d%E6%BF%80%E5%85%89slam%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/">2d激光slam学习笔记</a></span></div><div class="content">代码仓库https://github.com/peitianyu/2d_slam_learning.git
简介123456789102维坐标变换相对简单,实际上主要可分为两部分,求解转换矩阵或者转换向量/求解转换后坐标这里主要用两个函数表示 - TransformFrom() - TransformAdd()这里提供了两种思路 - 根据公式直接代入,硬解: 对于2dslam,建议使用,比较简单 - 齐次坐标使用Eigen库求解 - 使用四元数更新(待添加)位姿线性差分: - 对差分向量进行线性差分
测试123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960// 直接求解变换向量与变换后坐标void TestDirectSolution(){ Eigen::Vector3f old_pose(1.0, 1.0, 1.0); Eigen::Vector3f new_pose(2.0, 2.0, 2. ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/blog/2022/09/21/2022-9-21-0.-2d_slam_learning%E7%AE%80%E4%BB%8B/" title="0. 2d_slam_learning简介"> <img class="post_bg" src="https://s2.loli.net/2022/05/11/9Wg3sn1ktr8vObH.jpg" onerror="this.onerror=null;this.src='/blog/img/404.jpg'" alt="0. 2d_slam_learning简介"></a></div><div class="recent-post-info"><a class="article-title" href="/blog/2022/09/21/2022-9-21-0.-2d_slam_learning%E7%AE%80%E4%BB%8B/" title="0. 2d_slam_learning简介">0. 2d_slam_learning简介</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2022-09-21T04:30:12.000Z" title="发表于 2022-09-21 12:30:12">2022-09-21</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/blog/categories/slam/">slam</a><i class="fas fa-angle-right"></i><a class="article-meta__categories" href="/blog/categories/slam/2d-slam-learning/">2d_slam_learning</a></span><span class="article-meta tags"><span class="article-meta__separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/blog/tags/2d%E6%BF%80%E5%85%89slam%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/">2d激光slam学习笔记</a></span></div><div class="content">代码仓库https://github.com/peitianyu/2d_slam_learning.git
简介12纸上得来终觉浅,绝知此事要躬行.学习slam已经有一年多,总感觉对所学掌握的不够扎实,所以通过写教程的方式记录,方便以后复习之用
章节坐标变换
[里程计标定]
[激光数据处理]
[卡尔曼滤波]
[非线性优化]
[激光配准]
[概率地图]
[回环检测]
[图优化]
[因子图优化]
[重定位]
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