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作者：Frank
时间：2016-07-01

为了加深对Monocular SLAM的理解，开始学习并撰写一系列的博客，博客内容可能从最基本的相机标定开始，涉及单目相机的标定原理，各特征提取和匹配算子等系列内容。由于自己对SLAM相关的知识点理解的也不是非常透彻，所以博客序列也算是对自己知识的巩固和加强吧，文中若有错误，请不吝指出，谢谢！



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$$T_{k,k-1}=arg [\mathop {\min }\limits_T ] \sum_{i}||u^{‘}_i- \pi(p_i)||_{\sum}^2 \tag{2} $$

$$\left\{ {\matrix{
{v_0}=(B_{12}B_{13}-B_{11}B_{23}) / (B_{11}B_{22}-B_{12}^2) \cr
}}\right. \tag{29} $$

令$$B={A^{-T}}{A^{-1}}=\left[{ \matrix{
B_{11}&B_{12}&B_{13} \cr
B_{21}&B_{22}&B_{23} \cr
B_{31}&B_{32}&B_{33} \cr
}}\right] =\left[ {\matrix{
1 \over {\alpha ^2} & - \gamma \over{ {\alpha ^2}\beta } & {v_0} \gamma - {u_0} \beta \over {\alpha ^2} \beta \cr
- \gamma \over {\alpha ^2}\beta & {\gamma ^2 \over {\alpha ^2}{\beta ^2}} + {1 \over \beta ^2 } & { - \gamma ({v_0}\gamma - {u_0}\beta ) \over {\alpha ^2}{\beta ^2}} - {v_0 \over \beta ^2} \cr
{v_0} \gamma - {u_0} \beta \over {\alpha ^2} \beta & { - \gamma ({v_0}\gamma - {u_0}\beta ) \over {\alpha ^2}{\beta ^2}} - {v_0 \over \beta ^2} & {({v_0}\gamma - {u_0}\beta )^2 \over {\alpha ^2}{\beta ^2}} + {v_0^2 \over \beta ^2} + 1 \cr
} } \right]$$

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