Further tweaks to RANSAC algorithm and line of best fit
This commit is contained in:
207
map.txt
207
map.txt
@@ -1,109 +1,112 @@
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edge,end,39,start,59
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@@ -38,11 +38,13 @@ public class Car{
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}
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//draw the car and its views
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public void drawCar(PointGraph g){
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public void drawCar(PointGraph map, boolean SLAMIsHidden){
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proc.stroke(255);
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proc.ellipse(pose.x, pose.y, carWidth, carLength);
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this.updateScan(g);
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this.slam.drawFeatures(proc);
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this.updateScan(map);
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if(!SLAMIsHidden){
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this.slam.drawFeatures(proc);
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}
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}
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//With all the views that the car has, get their point list
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@@ -13,6 +13,7 @@ public class Processing extends PApplet {
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PointGraph map = new PointGraph();
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boolean mapIsHidden = false;
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boolean SLAMIsHidden = false;
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public static void main(String[] args) {
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PApplet.main("Processing");
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@@ -36,7 +37,7 @@ public class Processing extends PApplet {
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if(!mapIsHidden){
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map.draw(processing);
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}
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car.drawCar(map);
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car.drawCar(map, SLAMIsHidden);
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strokeWeight(2);
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stroke(255);
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//car.drive(new int[] {0, 0});
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@@ -95,6 +96,9 @@ public class Processing extends PApplet {
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if(key == 'h'){
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mapIsHidden = !mapIsHidden;
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}
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if(key == 'j'){
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SLAMIsHidden = !SLAMIsHidden;
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}
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}
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public void mousePressed(){
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@@ -11,7 +11,7 @@ import static processing.core.PApplet.*;
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public class SLAM{
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ArrayList<Line> lines = new ArrayList<>();
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ArrayList<Vector> unassociatedPoints = new ArrayList<>();
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ShortTermMem unassociatedPoints = new ShortTermMem();
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private static PApplet proc;
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SLAM(PApplet processing){
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@@ -27,7 +27,7 @@ public class SLAM{
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*/
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private List<Vector> randomSampleInAngleRange(ArrayList<Vector> set, int subSampleSize, float minAngle, float maxAngle){
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// create an arraylist with all points within the angle range fro mthe given set
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// create an arraylist with all points within the angle range from the given set
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ArrayList<Vector> pointsInAngleRange = new ArrayList<>();
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for(Vector point : set){
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if(minAngle <= point.z && point.z <= maxAngle){
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@@ -54,6 +54,7 @@ public class SLAM{
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private void extractFeature(List<Vector> randomSample, float maxRange, int consensus){
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// get a line of best fit for this list.
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Line bestFit = new Line(randomSample);
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// check that there are enough points in the sample that are less than the maxRange away to form a consensus
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int count = 0;
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ArrayList<Vector> newRandomSample = new ArrayList<>();
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for (Vector v : randomSample) {
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@@ -73,17 +74,35 @@ public class SLAM{
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}
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}
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private void fitToPreviousReadings(List<Vector> sample, float maxRange){
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// keep track of points that were succesffully associated so they can be removed from the sample at the end
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ArrayList<Vector> pointsToRemove = new ArrayList<>();
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// try to associate points from the smaple with pre-existing lines
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for(Vector v: sample){
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for(Line l : lines){
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if(l.getDistance(v) < maxRange){
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l.refitLine(v);
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pointsToRemove.add(v);
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}
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}
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}
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for(Vector v : pointsToRemove){
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sample.remove(v);
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}
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}
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/**
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* @param view a laser scan view
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*/
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public void RANSAC(View view){
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unassociatedPoints.addAll(view.getPoints());
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unassociatedPoints.addScan(view.getPos(), view.getPoints());
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float degreeRange = radians(10); // range to randomly sample readings within
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int numSampleReadings = 10; // number of readings to randomly sample
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float degreeRange = radians(5); // range to randomly sample readings within
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int numSampleReadings = 15; // number of readings to randomly sample
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int consensus = 7; // the number of points that need to lie near a line for it to be considered valid.
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float maxRange = 5; // the maximum distance a point can be away from the line for it to count as a consensus
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int consensus = 10; // the number of points that need to lie near a line for it to be considered valid.
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float maxRange = 10; // the maximum distance a point can be away from the line for it to count as a consensus
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// this for loop determines the maximum number of trials we're willing to do.
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for(int j = 0; j < 20; j++) {
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@@ -96,9 +115,11 @@ public class SLAM{
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float randomAngle = (float) (2*PI*(random()) - 0.5);
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// get a random sub sample of newPoints within the index range of a given size
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List<Vector> randomSample = this.randomSampleInAngleRange(this.unassociatedPoints, numSampleReadings, randomAngle-degreeRange, randomAngle+degreeRange);
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List<Vector> randomSample = this.randomSampleInAngleRange(this.unassociatedPoints.getPoints(), numSampleReadings, randomAngle-degreeRange, randomAngle+degreeRange);
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if(randomSample.size() >= numSampleReadings){
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// try to associate points from the sample with previously made lines
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fitToPreviousReadings(randomSample, maxRange);
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// check if the sub sample forms a valid line and remove the randomSample points if it does.
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extractFeature(randomSample, maxRange, consensus);
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}
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59
src/ShortTermMem.java
Normal file
59
src/ShortTermMem.java
Normal file
@@ -0,0 +1,59 @@
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import java.util.ArrayList;
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import Vector.Vector;
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public class ShortTermMem {
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ArrayList<ArrayList<Vector>> scans = new ArrayList<>();
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ArrayList<Vector> scanPositions = new ArrayList<>();
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ArrayList<Long> scanTimes = new ArrayList<>();
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private int size = 0;
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public void addScan(Vector scanPosition, ArrayList<Vector> scan){
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size += scan.size();
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scans.add(scan);
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scanPositions.add(scanPosition);
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scanTimes.add(System.currentTimeMillis());
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purgeScans();
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}
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public ArrayList<Vector> getPoints(){
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ArrayList<Vector> points = new ArrayList<>();
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for(ArrayList<Vector> pointList : this.scans){
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points.addAll(pointList);
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}
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return points;
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}
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public void remove(Vector point){
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for(ArrayList<Vector> pointList : this.scans){
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int listSize = pointList.size();
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pointList.remove(point);
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if(listSize - pointList.size() != 0){
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size--;
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break;
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||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private void purgeScans(){
|
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long currentTime = System.currentTimeMillis();
|
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int i = scanTimes.size();
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// loop through the list backwards and remove all scans that are over second old
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// we loop backwards to avoid removal conflicts
|
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while(i > 0){
|
||||
i--;
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long dt = currentTime - scanTimes.get(i);
|
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if(dt < 1000){
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continue;
|
||||
}
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size -= scans.get(i).size();
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scanTimes.remove(i);
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||||
scanPositions.remove(i);
|
||||
scans.remove(i);
|
||||
}
|
||||
}
|
||||
|
||||
public int size(){
|
||||
return this.size;
|
||||
}
|
||||
}
|
||||
@@ -3,6 +3,7 @@ package Vector;
|
||||
import Vector.Vector;
|
||||
import processing.core.PApplet;
|
||||
|
||||
import java.util.ArrayList;
|
||||
import java.util.List;
|
||||
|
||||
import static processing.core.PApplet.*;
|
||||
@@ -13,6 +14,8 @@ public class Line implements LineInterface{
|
||||
// store the starting position of the line
|
||||
protected Vector position = new Vector(0,0);
|
||||
|
||||
protected ArrayList<Vector> points = new ArrayList<>();
|
||||
|
||||
public Line(Vector startPosition, Vector endPosition){
|
||||
direction = endPosition.sub(startPosition);
|
||||
position = startPosition;
|
||||
@@ -26,8 +29,16 @@ public class Line implements LineInterface{
|
||||
bestFit(points);
|
||||
}
|
||||
|
||||
public void refitLine(Vector newPoint){
|
||||
// add the new point to our list
|
||||
this.points.add(newPoint);
|
||||
// rerun the bestFit algorithm with the new point
|
||||
bestFit(new ArrayList<>());
|
||||
}
|
||||
|
||||
// least squares line of best fit algorithm
|
||||
private void bestFit(List<Vector> points){
|
||||
public void bestFit(List<Vector> fitPoints){
|
||||
this.points.addAll(fitPoints);
|
||||
// get the mean of all the points
|
||||
Vector mean = new Vector();
|
||||
for(Vector point : points){
|
||||
@@ -48,7 +59,7 @@ public class Line implements LineInterface{
|
||||
length += dist;
|
||||
|
||||
}
|
||||
length = 2.5f*length/points.size();
|
||||
length = 2f*length/points.size();
|
||||
// if the direction is perfectly vertical create a line to represent that.
|
||||
if(direction.y == 0){
|
||||
this.direction = new Vector(0, 1);
|
||||
@@ -86,12 +97,15 @@ public class Line implements LineInterface{
|
||||
return this.position.add(this.direction);
|
||||
}
|
||||
|
||||
/**
|
||||
* @param point
|
||||
* @return the smallest distance from the point to this line
|
||||
*/
|
||||
public float getDistance(Vector point){
|
||||
return (point.sub(position).cross(direction)).mag() / direction.mag();
|
||||
float line_dist = direction.mag();
|
||||
if(line_dist == 0) return this.position.sub(point).mag();
|
||||
|
||||
Vector l2 = this.endPoint();
|
||||
float t = ((point.x - position.x) * (l2.x - position.x) + (point.y - position.y) * (l2.y - position.y) + (point.z - position.z) * (l2.z - position.z)) / line_dist;
|
||||
t = constrain(t, 0, 1);
|
||||
Vector closestPoint = new Vector(position.x + t * (l2.x - position.x), position.y + t * (l2.y - position.y), position.z + t * (l2.z - position.z));
|
||||
return closestPoint.sub(point).mag();
|
||||
}
|
||||
|
||||
public void draw(PApplet proc){
|
||||
|
||||
@@ -1,5 +1,7 @@
|
||||
package Vector;
|
||||
|
||||
import processing.core.PApplet;
|
||||
|
||||
import static java.lang.Math.*;
|
||||
import static processing.core.PApplet.cos;
|
||||
import static processing.core.PApplet.sin;
|
||||
@@ -90,4 +92,8 @@ public class Vector {
|
||||
float currentAngle = this.angle();
|
||||
return new Vector(cos(currentAngle + angle), sin(currentAngle + angle)).mul(distance);
|
||||
}
|
||||
|
||||
public void draw(PApplet proc){
|
||||
proc.circle(this.x, this.y, 8);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -35,7 +35,8 @@ public class View {
|
||||
for (Ray ray : rays) {
|
||||
ray.castRay(map);
|
||||
if(ray.hasCollided()){
|
||||
ray.drawRay(proc);
|
||||
ray.getPoint().draw(proc);
|
||||
// ray.drawRay(proc);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user