<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[kokerf的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/kokerf</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; kokerf]]></copyright><item><title><![CDATA[DSO之光度标定]]></title><link>https://blog.youkuaiyun.com/kokerf/article/details/80170257</link><guid>https://blog.youkuaiyun.com/kokerf/article/details/80170257</guid><author>kokerf</author><pubDate>Wed, 02 May 2018 18:50:47 +0800</pubDate><description><![CDATA[之前一直没看DSO1，前阵子对应光度模型的论文和代码研究了一下，做了简单的总结。



1. 相机光度模型（Photometric Model）

在光度标定的论文2中，作者给出光度模型如下：



I(x)=G(tV(x)B(x))(1)(1)I(x)=G(tV(x)B(x))
I(\mathbf x) = G(tV(\mathbf x)B(\mathbf x)) \tag{1}


这里的GG...]]></description><category></category></item><item><title><![CDATA[LSD-SLAM笔记之一致性约束]]></title><link>https://blog.youkuaiyun.com/kokerf/article/details/78006743</link><guid>https://blog.youkuaiyun.com/kokerf/article/details/78006743</guid><author>kokerf</author><pubDate>Sat, 16 Sep 2017 21:33:32 +0800</pubDate><description><![CDATA[LSD-SLAM 一致性约束　Sim3求解]]></description><category></category></item><item><title><![CDATA[LSD-SLAM笔记之DepthMap]]></title><link>https://blog.youkuaiyun.com/kokerf/article/details/78006703</link><guid>https://blog.youkuaiyun.com/kokerf/article/details/78006703</guid><author>kokerf</author><pubDate>Sat, 16 Sep 2017 21:29:19 +0800</pubDate><description><![CDATA[LSD-SLAM深度图估计]]></description><category></category></item><item><title><![CDATA[LSD-SLAM笔记之SE3Tracking]]></title><link>https://blog.youkuaiyun.com/kokerf/article/details/78005934</link><guid>https://blog.youkuaiyun.com/kokerf/article/details/78005934</guid><author>kokerf</author><pubDate>Sat, 16 Sep 2017 19:56:48 +0800</pubDate><description><![CDATA[LSD-SLAM SE3Tracking]]></description><category></category></item><item><title><![CDATA[从本质矩阵恢复相机矩阵]]></title><link>https://blog.youkuaiyun.com/kokerf/article/details/72911561</link><guid>https://blog.youkuaiyun.com/kokerf/article/details/72911561</guid><author>kokerf</author><pubDate>Thu, 08 Jun 2017 11:22:08 +0800</pubDate><description><![CDATA[本质矩阵

　　本质矩阵（essential matrix ）是基本矩阵在归一化图像坐标下的一种特殊形式。

　　考虑相机矩阵P=K[R|t]P=K[R|t]P=K[R|\mathbf t]，点x=PXx=PX\mathbf x=P\mathbf X是图像平面上的一个点，若已知相机内参KKK，那么点x^=K−1xx^=K−1x\mathbf{\hat x}=K^{-1}\mathbf x，有x^=...]]></description><category></category></item><item><title><![CDATA[单应矩阵分解]]></title><link>https://blog.youkuaiyun.com/kokerf/article/details/72885435</link><guid>https://blog.youkuaiyun.com/kokerf/article/details/72885435</guid><author>kokerf</author><pubDate>Tue, 06 Jun 2017 17:21:25 +0800</pubDate><description><![CDATA[单应矩阵分解　Faugeras SVD-based]]></description><category></category></item><item><title><![CDATA[三角形法恢复空间点深度]]></title><link>https://blog.youkuaiyun.com/kokerf/article/details/72844455</link><guid>https://blog.youkuaiyun.com/kokerf/article/details/72844455</guid><author>kokerf</author><pubDate>Fri, 02 Jun 2017 15:03:53 +0800</pubDate><description><![CDATA[三角形法恢复空间点深度

通常，在已知两个相机的相对位姿TT\mathbf T的情况下，得到在两个视图下的对应匹配点x↔x′x↔x′\bf x \leftrightarrow \bf x'，我们就可以求得该对应点在空间中的位置，也就是求得图像点的深度。接下来介绍两种求解方法。



1.求解空间点坐标

当我们得到两个视图的一组匹配点，我们希望能恢复出世界点在三维世界的坐标。这里就涉及到使用三角形...]]></description><category></category></item><item><title><![CDATA[估计问题]]></title><link>https://blog.youkuaiyun.com/kokerf/article/details/72673268</link><guid>https://blog.youkuaiyun.com/kokerf/article/details/72673268</guid><author>kokerf</author><pubDate>Wed, 24 May 2017 09:30:21 +0800</pubDate><description><![CDATA[直接线性变换 DLT 坐标归一化 RANSAC]]></description><category></category></item><item><title><![CDATA[基本矩阵的基本解法之8点算法]]></title><link>https://blog.youkuaiyun.com/kokerf/article/details/72630863</link><guid>https://blog.youkuaiyun.com/kokerf/article/details/72630863</guid><author>kokerf</author><pubDate>Mon, 22 May 2017 22:25:17 +0800</pubDate><description><![CDATA[基本矩阵　归一化 ８点算法 对极线]]></description><category></category></item><item><title><![CDATA[最小二乘解（Least-squares Minimization ）]]></title><link>https://blog.youkuaiyun.com/kokerf/article/details/72437294</link><guid>https://blog.youkuaiyun.com/kokerf/article/details/72437294</guid><author>kokerf</author><pubDate>Wed, 17 May 2017 20:38:12 +0800</pubDate><description><![CDATA[最小二乘解 Least-squares Minimization SVD QR分解 正规矩阵]]></description><category></category></item><item><title><![CDATA[基本矩阵、本质矩阵和单应矩阵]]></title><link>https://blog.youkuaiyun.com/kokerf/article/details/72191054</link><guid>https://blog.youkuaiyun.com/kokerf/article/details/72191054</guid><author>kokerf</author><pubDate>Mon, 15 May 2017 20:18:03 +0800</pubDate><description><![CDATA[基本矩阵、本质矩阵和单应矩阵]]></description><category></category></item><item><title><![CDATA[FAST(Features from Accelerated Segment Test)]]></title><link>https://blog.youkuaiyun.com/kokerf/article/details/70245300</link><guid>https://blog.youkuaiyun.com/kokerf/article/details/70245300</guid><author>kokerf</author><pubDate>Wed, 19 Apr 2017 21:06:07 +0800</pubDate><description><![CDATA[FAST原理以及代码详解]]></description><category></category></item><item><title><![CDATA[ROS：创建和发布自己的Message]]></title><link>https://blog.youkuaiyun.com/kokerf/article/details/51811835</link><guid>https://blog.youkuaiyun.com/kokerf/article/details/51811835</guid><author>kokerf</author><pubDate>Sat, 02 Jul 2016 17:36:24 +0800</pubDate><description><![CDATA[发布自己的ROS Message]]></description><category></category></item></channel></rss>