如何使用给定半径的圆拟合数据点

Question

我有一个来自管内部的激光扫描仪的测量点。 现在我想要拟合一个已知半径的圆管。 原则上类似于这篇文章。 但是我和R合作并在R中寻找解决方案。

数据来自LIDAR扫描仪，由角度信息和距离组成。 我测量一个我知道直径的管的内部。 我需要在管中的位置，这是圆的中心。 使用基于qr.solve的fitCircle函数，我可以为数据拟合一个圆。 但是，计算出的直径与每次测量的实际直径不同。 这是由于传感器的测量不准确以及管中的小干扰轮廓。 我现在想要将具有给定半径的圆拟合到数据中以提高准确性。

我知道计算可能很耗时，但我想测试精度提高了多少。

此图显示了没有直径限制的拟合结果：

# fitCircle, returns:
#   xf,yf = centre of the fitted circle
#   Rf = radius of the fitted circle
# based on:
#   x, y = raW datapoints
fitCircle <- function(x,y){
  a = qr.solve(cbind(x,y,rep(1,length(x))),cbind(-(x^2+y^2)))
  xf = -.5*a[1]
  yf = -.5*a[2]
  Rf  =  sqrt((a[1]^2+a[2]^2)/4-a[3])
  m <- cbind(xf,yf,Rf)
  return(m)
}

我的例子基于这些数据：

# some sample data from the LIDAR scanner
LIDARData <- read.table(header = TRUE, text = "
angle distance
0.1094 460.0
1.2344 460.0
2.4844 463.0
3.9844 463.0
5.2188 463.0
15.1719 460.0
16.4219 458.0
17.4063 462.0
18.9063 463.0
43.7656 463.0
44.9063 461.0
46.4063 460.0
47.5313 460.0
48.6719 461.0
55.0625 458.0
56.5781 459.0
57.4531 459.0
58.8438 458.0
60.0938 458.0
160.7656 435.0
162.0156 435.0
163.6406 433.0
168.2656 435.0
169.5000 436.0
170.8750 435.0
172.1250 435.0
173.6250 436.0
174.6250 436.0
176.1250 435.0
177.1250 421.0
178.7500 411.0
179.9844 419.0
180.7344 434.0
182.3594 429.0
183.3594 431.0
184.8594 432.0
185.8594 433.0
187.3594 435.0
188.6094 435.0
189.5938 435.0
191.0938 437.0
195.9531 438.0
196.9531 438.0
198.4531 436.0
")

以下是计算步骤：

# calculateCircle, returns:
#   xy = 2000 datapoints of a circle 
# based on:
#   x,y = centre of the circle
#   r = radius of the circle
calculateCircle <- function(x,y,r){
  x1 = (((0:2000)/1000)-1)
  x2 = (-1)*x1
  y1 = sqrt(1 - x1^2)
  y2 = (-1)*y1
  xr = ((c(x1,x2))*r)+x
  yr = ((c(y1,y2))*r)+y
  xy <- cbind(xr,yr)
  return(xy)
}

# calculate x, y coordinates based on angle and distance
LIDAR_x <- cos((LIDARData[[1]]*pi)/180)*LIDARData[[2]]
LIDAR_y <- sin((LIDARData[[1]]*pi)/180)*LIDARData[[2]]

plot(LIDAR_x,LIDAR_y,xlim=c(-500,500),ylim=c(-500,500), xlab="", ylab="", cex = 1.5, main="fit circle to raw data");par(new=TRUE)

# fit a circle to the data
fitdata <- fitCircle(LIDAR_x,LIDAR_y)

# calculate circle points to plot the caclutated circle
circledata <- calculateCircle(fitdata[1],fitdata[2],fitdata[3])
plot(circledata[,1],circledata[,2],xlim=c(-500,500),ylim=c(-500,500), xlab="", ylab="",col='red',type='l')

# draw the center point
points(fitdata[1],fitdata[2],pch=4,col="red",cex = 2);par(new=TRUE)
text(fitdata[1]+30,fitdata[2], sprintf("x=%.1fmm, y=%.1fmm", fitdata[1],fitdata[2]), adj = c(0,0), cex=1.0, col="red")

非常感谢您的帮助！

Answer 1

下面的代码使用嵌套for。 有点hacky但它​​应该做的工作。

# Convert data points to xy
LIDARData$x <- LIDARData$distance*cos(LIDARData$angle)
LIDARData$y <- LIDARData$distance*sin(LIDARData$angle)
plot(LIDARData$x, LIDARData$y)

# Create an equivalent theoretical circle using the average radius
circle_center_x = 0
circle_center_y = 0
circle_radius = mean(LIDARData$distance)
LIDARData$circle_x <- circle_radius*cos(LIDARData$angle) + circle_center_x
LIDARData$circle_y <- circle_radius*sin(LIDARData$angle) + circle_center_y
points(LIDARData$circle_x , LIDARData$circle_y, col = "red")
library(plotrix)
draw.circle(0,0,circle_radius, border="red")

# create dataframe to hold results
results <- data.frame(circle_center_x = c(NA),circle_center_y = c(NA),dist_sum = c(NA))

# Vary the center point ±50 and calculate distances from red point to black point.  
# Where the sum of those distances are minimized, we have found our ideal center point.
for (i in c(-50:50)){
for (j in c(-50:50)){
    print(paste(i, ":", j))
    circle_center_x = 0 + i
    circle_center_y = 0 + j
    LIDARData$circle_x <- circle_radius*cos(LIDARData$angle) + circle_center_x
    LIDARData$circle_y <- circle_radius*sin(LIDARData$angle) + circle_center_y    
    LIDARData$dist_to_circle <- sqrt( (LIDARData$x - LIDARData$circle_x)^2 + (LIDARData$y - LIDARData$circle_y)^2)
    # Save results to a dataframe
    results <- rbind(results, data.frame(circle_center_x = c(circle_center_x),circle_center_y = c(circle_center_y), dist_sum = c(sum(LIDARData$dist_to_circle))))
}
}

# Show the minimized center point
subset(results, dist_sum == min(results$dist_sum, na.rm=TRUE))

# circle_center_x circle_center_y dist_sum
#               0              -1 601.6528
# Here we see that our true center is 0, -1 and our ideal circle can be drawn from there.
plot(LIDARData$x, LIDARData$y)
draw.circle(0,-1,400, border="red")

---

使用此数据：

# Create sample dataframe
LIDARData <- read.table(header = TRUE, text = "
angle distance
0.1094 460.0
1.2344 460.0
2.4844 463.0
3.9844 463.0
5.2188 463.0
15.1719 460.0
16.4219 458.0
17.4063 462.0
18.9063 463.0
43.7656 463.0
44.9063 461.0
46.4063 460.0
47.5313 460.0
48.6719 461.0
55.0625 458.0
56.5781 459.0
57.4531 459.0
58.8438 458.0
60.0938 458.0
160.7656 435.0
162.0156 435.0
163.6406 433.0
168.2656 435.0
169.5000 436.0
170.8750 435.0
172.1250 435.0
173.6250 436.0
174.6250 436.0
176.1250 435.0
177.1250 421.0
178.7500 411.0
179.9844 419.0
180.7344 434.0
182.3594 429.0
183.3594 431.0
184.8594 432.0
185.8594 433.0
187.3594 435.0
188.6094 435.0
189.5938 435.0
191.0938 437.0
195.9531 438.0
196.9531 438.0
198.4531 436.0
")