First semi-successful test of feature extraction. I think the algorithm for line of best fit needs some work. Specifically, ifnding the starting and end point for the line.

This commit is contained in:
Quinn
2023-05-02 23:51:59 -05:00
parent 2f46605837
commit 7298f80d36
4 changed files with 176 additions and 119 deletions

184
map.txt
View File

@@ -1,79 +1,109 @@
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View File

@@ -42,18 +42,15 @@ public class Car{
proc.stroke(255); proc.stroke(255);
proc.ellipse(pose.x, pose.y, carWidth, carLength); proc.ellipse(pose.x, pose.y, carWidth, carLength);
this.updateScan(g); this.updateScan(g);
// this.slam.drawLines(); this.slam.drawFeatures(proc);
} }
//With all the views that the car has, get their point list //With all the views that the car has, get their point list
void updateScan(PointGraph map){ void updateScan(PointGraph map){
for(View view : views){ for(View view : views){
view.look(map); view.look(map);
} slam.RANSAC(view);
for(View view : views){
ArrayList<Vector> pointList = view.getPoints();
// slam.RANSAC(pointList, view.getFOV() / view.getRayNum());
} }
} }

View File

@@ -1,14 +1,17 @@
import Vector.*; import Vector.*;
import processing.core.*; import processing.core.*;
import java.lang.reflect.Array;
import java.util.ArrayList; import java.util.ArrayList;
import java.util.Collections; import java.util.Collections;
import java.util.List; import java.util.List;
import static java.lang.Math.random;
import static processing.core.PApplet.*; import static processing.core.PApplet.*;
public class SLAM{ public class SLAM{
ArrayList<Line> lines = new ArrayList<>(); ArrayList<Line> lines = new ArrayList<>();
ArrayList<Vector> unassociatedPoints = new ArrayList<>();
private static PApplet proc; private static PApplet proc;
SLAM(PApplet processing){ SLAM(PApplet processing){
@@ -17,36 +20,38 @@ public class SLAM{
/** /**
* @param set the set to take a sub sample of * @param set the set to take a sub sample of
* @param indexRange the range within to take the sub sample
* @param subSampleSize the size of the sub sample * @param subSampleSize the size of the sub sample
* @return A random subset of the set within an indexRange and of size: subSampleSize * @param minAngle the minimum angle allowed in the subset
* @param maxAngle the maximum angle allowed in the subset
* @return A random subset of the set within the angle range
*/ */
private List<Vector> randomSample(ArrayList<Vector> set, int indexRange, int subSampleSize){ private List<Vector> randomSampleInAngleRange(ArrayList<Vector> set, int subSampleSize, float minAngle, float maxAngle){
// select a random laser data reading
int randomIdx = (int) proc.random(set.size() - 1); // index of starter reading
Vector point = set.get(randomIdx); // point of starter reading
// get a random sample of size numSampleReadings within degreeRange degrees of this laser reading. // create an arraylist with all points within the angle range fro mthe given set
List<Vector> subSample; ArrayList<Vector> pointsInAngleRange = new ArrayList<>();
int rangeStart = randomIdx - indexRange >= 0 ? randomIdx - indexRange : 0; for(Vector point : set){
int rangeEnd = randomIdx + indexRange < set.size() ? randomIdx + indexRange : set.size()-1; if(minAngle <= point.z && point.z <= maxAngle){
subSample = set.subList(rangeStart, rangeEnd); // get the sub-sample pointsInAngleRange.add(point);
Collections.shuffle(subSample); // shuffle the list }
List<Vector> randomSample = subSample.subList(0, rangeEnd-rangeStart); // get our random sample
if (!randomSample.contains(point)) {
randomSample.add(point);
} }
return randomSample; // shuffle the list to randomize it
Collections.shuffle(pointsInAngleRange);
// if the list is too small, just return the whole list
if(pointsInAngleRange.size() < subSampleSize){
return pointsInAngleRange;
}
// return a subSample of the list
return pointsInAngleRange.subList(0, subSampleSize);
} }
/** /**
* @param originalList the list which the randomSample of points originated from
* @param randomSample a random subsampling of points from the originalList * @param randomSample a random subsampling of points from the originalList
* @param maxRange the maximum distance away from the line of best fit of the subSample of points for a given point's consensus to count. * @param maxRange the maximum distance away from the line of best fit of the subSample of points for a given point's consensus to count.
* @param consensus the number of points that have to give their consensus for the line of best fit to count as a valid feature. * @param consensus the number of points that have to give their consensus for the line of best fit to count as a valid feature.
*/ */
private void extractFeature(ArrayList<Vector> originalList, List<Vector> randomSample, float maxRange, int consensus){ private void extractFeature(List<Vector> randomSample, float maxRange, int consensus){
// get a line of best fit for this list. // get a line of best fit for this list.
Line bestFit = new Line(randomSample); Line bestFit = new Line(randomSample);
int count = 0; int count = 0;
@@ -63,41 +68,47 @@ public class SLAM{
lines.add(bestFit); lines.add(bestFit);
// remove the associated readings from the total available readings. // remove the associated readings from the total available readings.
for (Vector v : newRandomSample) { for (Vector v : newRandomSample) {
originalList.remove(v); this.unassociatedPoints.remove(v);
} }
} }
} }
/** /**
* @param newPoints a new scan of points to perform feature detection on * @param view a laser scan view
* @param raysPerDegree How many degrees apart are each ray that was cast
*/ */
public void RANSAC(ArrayList<Vector> newPoints, float raysPerDegree){ public void RANSAC(View view){
float degreeRange = radians(10/2); // range to randomly sample readings within unassociatedPoints.addAll(view.getPoints());
int indexRange = (int) (degreeRange / raysPerDegree);
float degreeRange = radians(25/2); // range to randomly sample readings within
int numSampleReadings = 10; // number of readings to randomly sample int numSampleReadings = 10; // number of readings to randomly sample
// constrain numSampleReadings so that it cant be higher than possible
if(numSampleReadings >= 2 * indexRange){ int consensus = 7; // the number of points that need to lie near a line for it to be considered valid.
numSampleReadings = 2 * indexRange; float maxRange = 5; // the maximum distance a point can be away from the line for it to count as a consensus
}
int consensus = 6; // the number of points that need to lie near a line for it to be considered valid.
float maxRange = 10; // the maximum distance a point can be away from the line for it to count as a consensus
// this for loop determines the maximum number of trials we're willing to do. // this for loop determines the maximum number of trials we're willing to do.
for(int j = 0; j < 20; j++) { for(int j = 0; j < 20; j++) {
// if there aren't enough points left in the set to form a consensus, we're done. // if there aren't enough points left in the set to form a consensus, we're done.
if(newPoints.size() < consensus){ if(this.unassociatedPoints.size() < maxRange){
break; break;
} }
// get a random angle between -PI and PI
float randomAngle = (float) (2*PI*(random()) - 0.5);
// get a random sub sample of newPoints within the index range of a given size // get a random sub sample of newPoints within the index range of a given size
List<Vector> randomSample = this.randomSample(newPoints, indexRange, numSampleReadings); List<Vector> randomSample = this.randomSampleInAngleRange(this.unassociatedPoints, numSampleReadings, randomAngle-degreeRange, randomAngle+degreeRange);
// check if the sub sample forms a valid line and remove the randomSample points if it does. // check if the sub sample forms a valid line and remove the randomSample points if it does.
extractFeature(newPoints, randomSample, maxRange, consensus); extractFeature(randomSample, maxRange, consensus);
} }
} }
public void drawFeatures(PApplet proc){
for(Line line : lines){
line.draw(proc);
}
}
} }

View File

@@ -22,7 +22,7 @@ public class View {
public void setRayNum(int numberOfRays, float FOV, float angleOffset) { public void setRayNum(int numberOfRays, float FOV, float angleOffset) {
float rayStep = FOV / numberOfRays; float rayStep = FOV / numberOfRays;
rays.clear(); rays.clear();
float angle = (float) (0.01 - angleOffset); //the 0.01 fixes some bugs float angle = (float) (angleOffset); //the 0.01 fixes some bugs
for (int i = 0; i < numberOfRays; i++) { for (int i = 0; i < numberOfRays; i++) {
Ray ray = new Ray(pose, angle); Ray ray = new Ray(pose, angle);
angle = angle + rayStep; angle = angle + rayStep;
@@ -51,7 +51,10 @@ public class View {
//changes the angle of the view //changes the angle of the view
public void setAngle(float angle) { public void setAngle(float angle) {
this.angle = angle; this.angle = angle;
this.setRayNum(rays.size(), this.FOV, angle); for(Ray ray : rays){
float angleOffset = ray.getAngle() - this.angle;
ray.setAngle(this.angle+angleOffset);
}
} }
//changes the field of view of the view //changes the field of view of the view
@@ -82,10 +85,26 @@ public class View {
for (Ray ray : rays) { for (Ray ray : rays) {
if(ray.hasCollided()){ if(ray.hasCollided()){
points.add(ray.getPoint()); Vector point = ray.getPoint();
// store the angle information for that point in the z coordinate
point.z = ray.getAngle();
points.add(point);
} }
} }
return points; return points;
} }
/**
* @return A list of the angles where a collision was detected
*/
public ArrayList<Float> getAngles(){
ArrayList<Float> angles = new ArrayList<>();
for (Ray ray : rays) {
if (ray.hasCollided()){
angles.add(ray.getAngle());
}
}
return angles;
}
} }