激光的前端配准算法

妖狐艹你老母 2022-11-14 10:22 174阅读 0赞

### 激光的前端配准算法 ###

*  1、ICP 匹配算法
 *  2、PL-ICP匹配算法
 *  3、基于优化的匹配方法
 *  4、相关匹配方法及分支定界加速
 *   *  4.1 帧间匹配似然场
     *  4.2 算法流程
     *  位姿搜索
     *  分枝定界算法

在SLAM形成回环之前，是一个LO算法。  
视觉可以通过特征描述符feature descriptor来判断两副图像是否是同一副图像，但是对激光来说没什么太大的区别。  
框太大，计算量大，可区分性低，形成错误的回环。  
基本上来说，前端配准越好，后面的效果也越好  
并不一定是前后两帧进行匹配，也有可能是前n帧形成的submap跟当前n+1帧进行匹配，所以叫 map 2 scan。

# 1、ICP 匹配算法 #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70 1]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70 2]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70 3]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70 4]

激光数据的缺点： 数据是离散的，墙面是连续的，就会产生问题，下一帧也打到墙上，会产生匹配误差，带来额外的误差。

# 2、PL-ICP匹配算法 #

PL-ICP发现了激光的缺点  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70 5]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70 6]PL-ICP 更能反应实际情况，对初值比较敏感，要不然容易陷入局部误差值。

# 3、基于优化的匹配方法 #

梯度优化  
开源： HectorSLAM 不用里程计  
Cartographer CSM加梯度优化  
高斯牛顿方法进行求解  
下面介绍基于优化的梯度方法  
容易陷入局部极值  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70 7]  
我们想要的地图匹配程度越高，那就是地图占用概率最高， M i M\_i Mi最大，1- M i M\_i Mi最小  
似然场。  
第 i i i个激光，激光越大，重合度越好。

求梯度，似然场，栅格话，离散。  
怎么对地图进行插值。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70 8]  
转化到map坐标系。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70 9]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70 10]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70 11]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70 12]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70 13]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70 14]  
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优化的问题就是初始值敏感，但是精度高。

# 4、相关匹配方法及分支定界加速 #

C(相关)SM

## 4.1 帧间匹配似然场 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70 16]

## 4.2 算法流程 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70 17]

## 位姿搜索 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70 18]

## 分枝定界算法 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0NDQ3J1bm5lcg_size_16_color_FFFFFF_t_70 19]  
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