轮式里程计标定及二维激光数据去畸变

- 日理万妓 2023-01-18 02:27 117阅读 0赞

### 二维激光数据去畸变 ###

*   *  00.引言
     *  01.里程计标定
     *  02.二维激光雷达畸变校正

## 00.引言 ##

二维激光数据结构查看`rosmsg show sensor_msgs/LaserScan`：

std_msgs/Header header	//　数据的消息头
      uint32 seq			//　数据的序号
      time stamp			//　数据的时间戳
      string frame_id		//　数据的坐标系
    float32 angle_min		//　雷达数据的起始角度（最小角度）
    float32 angle_max		//　雷达数据的终止角度（最大角度）
    float32 angle_increment	//　雷达数据的角度分辨率（角度增量）
    float32 time_increment	//　雷达数据每个数据点的时间间隔
    float32 scan_time		//　当前帧数据与下一帧数据的时间间隔
    float32 range_min		//　雷达数据的最小值
    float32 range_max		//　雷达数据的最大值
    float32[] ranges		//　雷达数据每个点对应的在极坐标系下的距离值
    float32[] intensities	//　雷达数据每个点对应的强度值

实际数据距离：

header: 
      seq: 3568
      stamp: 
        secs: 1510031272
        nsecs: 522861940
      frame_id: "base_laser"
    angle_min: -1.5708758831
    angle_max: 1.5762219429
    angle_increment: 0.00581718236208
    time_increment: 6.17219993728e-05
    scan_time: 0.0666666701436
    range_min: 0.0500000007451
    range_max: 25.0
    ranges: [6.2769999504089355, 6.285999774932861, 6.296000003814697, 6.303999900817871, 6.315000057220459, 6.322999954223633, 6.313000202178955, 6.186999797821045, 6.116000175476074, 6.123000144958496, 6.13100004196167, 6.145999908447266, 6.1579999923706055, 6.163000106811523, 6.183000087738037, 6.193999767303467, 6.203000068664551, 6.2170000076293945, 6.234000205993652, 6.256999969482422, 6.306000232696533, 6.285999774932861, 6.290999889373779, 6.303999900817871, 6.318999767303467, 6.329999923706055, 6.3460001945495605, 6.359000205993652, 6.38100004196167, 6.394999980926514, 6.409999847412109, 6.428999900817871, 6.445000171661377, 6.460000038146973, 6.486000061035156, 6.494999885559082, 6.514999866485596, 6.5370001792907715, 6.546999931335449, 6.572999954223633, 6.590000152587891, 6.607999801635742, 6.636000156402588, 6.6529998779296875, 6.660999774932861, 10.97700023651123, 10.920000076293945, 11.164999961853027, 11.23799991607666, 0.0, 0.0, 0.0, 0.0, 14.932000160217285, 0.0, 0.0, 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    intensities: [113.0, 116.0, 110.0, 110.0, 113.0, 116.0, 93.0, 79.0, 96.0, 106.0, 103.0, 100.0, 106.0, 103.0, 100.0, 103.0, 103.0, 96.0, 103.0, 100.0, 86.0, 96.0, 100.0, 100.0, 93.0, 100.0, 100.0, 100.0, 103.0, 100.0, 100.0, 103.0, 100.0, 100.0, 103.0, 96.0, 96.0, 96.0, 100.0, 100.0, 100.0, 93.0, 96.0, 100.0, 66.0, 39.0, 42.0, 32.0, 22.0, 0.0, 0.0, 0.0, 0.0, 12.0, 0.0, 0.0, 0.0, 19.0, 49.0, 53.0, 49.0, 53.0, 56.0, 59.0, 22.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 5.0, 63.0, 83.0, 90.0, 86.0, 86.0, 90.0, 96.0, 100.0, 100.0, 100.0, 113.0, 106.0, 0.0, 32.0, 0.0, 0.0, 63.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 59.0, 153.0, 160.0, 160.0, 160.0, 160.0, 167.0, 163.0, 160.0, 170.0, 167.0, 167.0, 167.0, 167.0, 163.0, 167.0, 167.0, 174.0, 170.0, 170.0, 170.0, 167.0, 143.0, 133.0, 130.0, 157.0, 167.0, 170.0, 170.0, 177.0, 177.0, 174.0, 174.0, 174.0, 174.0, 170.0, 170.0, 170.0, 170.0, 174.0, 170.0, 177.0, 177.0, 174.0, 180.0, 180.0, 180.0, 167.0, 147.0, 147.0, 150.0, 140.0, 143.0, 143.0, 137.0, 140.0, 143.0, 133.0, 137.0, 133.0, 133.0, 133.0, 130.0, 120.0, 123.0, 123.0, 133.0, 137.0, 116.0, 116.0, 110.0, 110.0, 110.0, 113.0, 106.0, 106.0, 106.0, 100.0, 96.0, 106.0, 110.0, 113.0, 110.0, 103.0, 56.0, 46.0, 59.0, 53.0, 79.0, 73.0, 79.0, 83.0, 86.0, 83.0, 86.0, 79.0, 86.0, 79.0, 90.0, 93.0, 96.0, 93.0, 93.0, 96.0, 96.0, 90.0, 93.0, 90.0, 86.0, 93.0, 93.0, 86.0, 86.0, 90.0, 86.0, 83.0, 90.0, 83.0, 42.0, 56.0, 59.0, 63.0, 63.0, 59.0, 63.0, 63.0, 66.0, 69.0, 63.0, 63.0, 63.0, 69.0, 63.0, 63.0, 56.0, 59.0, 63.0, 59.0, 63.0, 66.0, 63.0, 59.0, 66.0, 63.0, 56.0, 46.0, 56.0, 59.0, 49.0, 53.0, 53.0, 56.0, 63.0, 63.0, 59.0, 59.0, 63.0, 63.0, 53.0, 46.0, 42.0, 49.0, 46.0, 46.0, 46.0, 46.0, 46.0, 3.0, 16.0, 12.0, 0.0, 0.0, 0.0, 19.0, 42.0, 49.0, 0.0, 26.0, 63.0, 93.0, 100.0, 103.0, 100.0, 100.0, 103.0, 103.0, 100.0, 103.0, 103.0, 100.0, 100.0, 90.0, 90.0, 100.0, 103.0, 103.0, 103.0, 103.0, 106.0, 110.0, 100.0, 86.0, 66.0, 59.0, 56.0, 66.0, 63.0, 73.0, 79.0, 76.0, 76.0, 83.0, 79.0, 79.0, 86.0, 79.0, 76.0, 86.0, 83.0, 106.0, 120.0, 140.0, 123.0, 76.0, 90.0, 86.0, 90.0, 83.0, 86.0, 90.0, 90.0, 86.0, 90.0, 90.0, 90.0, 103.0, 143.0, 184.0, 190.0, 211.0, 217.0, 230.0, 230.0, 230.0, 230.0, 230.0, 230.0, 217.0, 207.0, 211.0, 204.0, 194.0, 187.0, 184.0, 177.0, 174.0, 170.0, 163.0, 167.0, 167.0, 170.0, 174.0, 167.0, 167.0, 167.0, 160.0, 167.0, 167.0, 160.0, 160.0, 167.0, 167.0, 167.0, 163.0, 163.0, 167.0, 163.0, 184.0, 194.0, 150.0, 110.0, 113.0, 106.0, 106.0, 110.0, 106.0, 113.0, 116.0, 153.0, 170.0, 174.0, 174.0, 174.0, 137.0, 150.0, 160.0, 160.0, 160.0, 157.0, 157.0, 153.0, 157.0, 157.0, 160.0, 153.0, 157.0, 157.0, 157.0, 157.0, 157.0, 153.0, 160.0, 153.0, 153.0, 157.0, 150.0, 153.0, 153.0, 147.0, 130.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 147.0, 180.0, 177.0, 167.0, 140.0, 19.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 130.0, 160.0, 160.0, 130.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 63.0, 140.0, 160.0, 160.0, 160.0, 157.0, 157.0, 157.0, 160.0, 153.0, 150.0, 160.0, 153.0, 160.0, 140.0, 133.0, 184.0, 187.0, 184.0, 174.0, 133.0, 180.0, 184.0, 177.0, 160.0, 170.0, 177.0, 174.0, 170.0, 167.0, 170.0, 174.0, 180.0, 177.0, 180.0, 184.0, 177.0, 180.0, 177.0, 184.0, 177.0, 177.0, 180.0, 180.0, 177.0, 180.0, 180.0, 177.0, 180.0, 180.0, 180.0, 177.0, 174.0, 174.0, 174.0, 174.0, 170.0, 170.0, 167.0, 163.0, 167.0, 163.0, 163.0, 163.0, 163.0, 160.0, 163.0, 163.0, 160.0, 160.0, 160.0, 110.0, 174.0, 174.0, 177.0, 153.0, 153.0, 157.0, 153.0, 160.0, 153.0, 59.0, 180.0, 190.0, 190.0, 194.0]
    ---

rviz显示原始数据：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZiXzk0MTIxOQ_size_16_color_FFFFFF_t_70_pic_center]

## 01.里程计标定 ##

这里的里程计指的是轮式里程计。轮式里程计子啊进行航迹推算时需要使用到轮间距等机器人固有属性参数，虽然出厂会给出参数，但也不是十分标准，类似相机一样需要标定一下。

标定时，使用直接线性方程法进行标定，其实最后标定的参数也不是机器人固有参数，而是得出轮式里程计位姿与"真实"位姿之间（一般通过激光匹配获取）的一个变换矩阵。

*  [参考博客][Link 1]
 *  注意两帧之间的相对位姿求解，假设机器人从第 i i i时刻(轮式里程计path为 T i T\_i Ti)运动至第  j j j时刻(轮式里程计path为 T j T\_j Tj)，则相对位姿为： T i − 1 . T j T\_i^\{-1\}.T\_j Ti−1.Tj
 *  航迹推算中的path更新为： \[ x y θ \] = \[ x y θ \] + \[ cos ⁡ θ − sin ⁡ θ 0 sin ⁡ θ cos ⁡ θ 0 0 0 1 \] \[ d x d y d θ \] \\left\[\\begin\{array\}\{l\} x \\\\ y \\\\ \\theta \\end\{array\}\\right\]=\\left\[\\begin\{array\}\{l\} x \\\\ y \\\\ \\theta \\end\{array\}\\right\]+\\left\[\\begin\{array\}\{ccc\} \\cos \\theta & -\\sin \\theta & 0 \\\\ \\sin \\theta & \\cos \\theta & 0 \\\\ 0 & 0 & 1 \\end\{array\}\\right\]\\left\[\\begin\{array\}\{l\} d x \\\\ d y \\\\ d \\theta \\end\{array\}\\right\] ⎣⎡xyθ⎦⎤=⎣⎡xyθ⎦⎤\+⎣⎡cosθsinθ0−sinθcosθ0001⎦⎤⎣⎡dxdydθ⎦⎤

最后一维求解出来感觉有问题：

3.95997e-316 6.95292e-310 6.95292e-310
    6.95292e-310 6.95292e-310 3.20386e-317
               0 6.95292e-310         -nan

有知道原因的小伙伴帮忙指出一下。

通过激光配准的相对位姿来求解映射矩阵，然后将校正后的数据进行发布。

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZiXzk0MTIxOQ_size_16_color_FFFFFF_t_70_pic_center 1]

## 02.二维激光雷达畸变校正 ##

待更。

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZiXzk0MTIxOQ_size_16_color_FFFFFF_t_70_pic_center]: /images/20221021/fc42806d241e40698ef89111e6e90152.png
[Link 1]: https://blog.csdn.net/xululala/article/details/105997343
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2ZiXzk0MTIxOQ_size_16_color_FFFFFF_t_70_pic_center 1]: /images/20221021/b16e2cc992174041b0512e34b2706858.png