Exemple #1
0
  // public Point2D.Double localize(double odo_x, double odo_y, double odo_theta, List<Fiducial>
  // fiducials){
  public Point2D.Double localize(double odo_x, double odo_y, List<Fiducial> fiducials) {
    double x_disp = odo_x - prev_odo_x;
    double y_disp = odo_y - prev_odo_y;

    prev_odo_x = odo_x;
    prev_odo_y = odo_y;

    // double theta_disp = odo_theta - prev_odo_theta;
    // prev_odo_theta = odo_theta;

    List<Point2D.Double> new_particles = new ArrayList<Point2D.Double>();
    List<Double> new_probs = new ArrayList<Double>();
    List<Double> cumulative_probs = new ArrayList<Double>();

    Point2D.Double curr_point;
    double curr_prob, distance, mapx, mapy;
    double probsum = 0;
    for (int i = 0; i < NUM_PARTICLES; i++) {
      // Motion Update
      curr_point = new_particles.get(i);
      new_particles.add(new Point2D.Double(curr_point.x + x_disp, curr_point.y + y_disp));

      // Sensor Update
      curr_prob = 1;
      for (Fiducial fid : fiducials) {
        mapx = fiducialPairs.get(fid.colors).x;
        mapy = fiducialPairs.get(fid.colors).y;

        distance =
            Math.sqrt(
                (curr_point.x + x_disp - mapx) * (curr_point.x + x_disp - mapx)
                    + (curr_point.y + y_disp - mapy) * (curr_point.y + y_disp - mapy));

        curr_prob *=
            (Math.exp(-0.5 * (fid.distance - distance) * (fid.distance - distance) / SIGMA)
                / Math.sqrt(2 * Math.PI * SIGMA * SIGMA));
      }

      probsum += curr_prob;
      new_probs.add(curr_prob);
      cumulative_probs.add(probsum);
    }

    particles.clear();
    double rand_val;
    int index;
    int max_index = 0;
    for (int i = 0; i < NUM_PARTICLES; i++) {
      rand_val = probsum * Math.random();
      index = Math.abs(Collections.binarySearch(cumulative_probs, rand_val));

      particles.add(new_particles.get(index));

      if (new_probs.get(max_index) < new_probs.get(i)) {
        max_index = i;
      }
    }

    localPoint = new_particles.get(max_index);

    return localPoint;
  }
  public OdoPoint localize(
      double odo_x, double odo_y, double odo_theta, List<FiducialBlob> fiducials) {
    double x_disp = odo_x - prev_odo_x;
    double y_disp = odo_y - prev_odo_y;
    double theta_disp = odo_theta - prev_odo_theta;

    prev_odo_x = odo_x;
    prev_odo_y = odo_y;
    prev_odo_theta = odo_theta;

    localPoint =
        new OdoPoint(localPoint.x + x_disp, localPoint.y + y_disp, localPoint.theta + theta_disp);

    List<FiducialBlob> revFiducials = new ArrayList<FiducialBlob>();
    double mapx, mapy;
    Color[] colors = new Color[2];
    Fiducial mapFid;

    double mapbearing;
    double localAngle = localPoint.theta;
    localAngle = localAngle - ((int) (localAngle / (2 * Math.PI)));
    if (localAngle > Math.PI) {
      localAngle -= 2 * Math.PI;
    }

    List<OdoPoint> new_particles = new ArrayList<OdoPoint>();
    List<Double> new_probs = new ArrayList<Double>();
    List<Double> cumulative_probs = new ArrayList<Double>();

    OdoPoint curr_point;
    double curr_prob, distance, fid_distance, fid_bearing;
    double probsum = 0;

    // Filter out unreasonable fiducials
    for (FiducialBlob fid : fiducials) {
      colors[0] = fid.colors[0];
      colors[1] = fid.colors[1];

      if (findFiducial(colors[0], colors[1]) != null) {
        mapFid = findFiducial(colors[0], colors[1]);
        mapx = mapFid.getPosition().x;
        mapy = mapFid.getPosition().y;

        mapbearing = Math.atan2(mapy - localPoint.y, mapx - localPoint.x);

        distance =
            Math.sqrt(
                (localPoint.x - mapx) * (localPoint.x - mapx)
                    + (localPoint.y - mapy) * (localPoint.y - mapy));

        fid_distance = ImageBlob.computeDistance(fid.size, mapFid.getTopSize());

        System.out.println(
            "Localization: map and camera bearing = " + mapbearing + ", " + localAngle);
        System.out.println(
            "Localization: map and camera distance = " + distance + ", " + fid_distance);

        if ((Math.abs(mapbearing - localAngle) < Math.PI / 6
                || Math.abs(mapbearing - localAngle + 2 * Math.PI) < Math.PI / 6
                || Math.abs(mapbearing - localAngle - 2 * Math.PI) < Math.PI / 6)
            && fid_distance < distance * 1.3
            && distance < fid_distance * 1.3) {

          System.out.println("Localization: added fiducial");
          revFiducials.add(fid);
        } else {
          System.out.println("Localization: eliminated fiducial from bearing and distance");
        }
      } else {
        System.out.println("Localization: eliminated fiducial from colors");
      }
    }

    /*// Use Odometry if no fiducials
    if (revFiducials.size() == 0){
    	System.out.println("Localization: no fiducials to localize with");
    	particles.clear();
    	OdoPoint curr_point;
    	for (int i = 0; i < NUM_PARTICLES; i++){
    		curr_point = particles.get(i);
    		particles.add(new OdoPoint(curr_point.x + x_disp, curr_point.y + y_disp, curr_point.theta + theta_disp));
    	}
    	return localPoint;
    }*/

    for (int i = 0; i < NUM_PARTICLES; i++) {
      // Motion Update
      curr_point = particles.get(i);
      new_particles.add(
          new OdoPoint(
              curr_point.x + x_disp, curr_point.y + y_disp, curr_point.theta + theta_disp));

      // Sensor Update
      curr_prob = 1;
      for (FiducialBlob fid : revFiducials) {
        colors[0] = fid.colors[0];
        colors[1] = fid.colors[1];

        mapFid = findFiducial(colors[0], colors[1]);
        mapx = mapFid.getPosition().x;
        mapy = mapFid.getPosition().y;

        distance =
            Math.sqrt(
                (curr_point.x + x_disp - mapx) * (curr_point.x + x_disp - mapx)
                    + (curr_point.y + y_disp - mapy) * (curr_point.y + y_disp - mapy));

        fid_distance = ImageBlob.computeDistance(fid.size, mapFid.getTopSize());

        curr_prob *=
            NORMALIZATION
                * (Math.exp(
                        -0.5 * (fid_distance - distance) * (fid_distance - distance) / DIST_SIGMA)
                    / Math.sqrt(2 * Math.PI * DIST_SIGMA * DIST_SIGMA));

        fid_bearing = curr_point.theta + theta_disp - ImageBlob.computeBearing(fid.x);
        fid_bearing = fid_bearing - ((int) (fid_bearing / (2 * Math.PI)));
        if (fid_bearing > Math.PI) {
          fid_bearing -= 2 * Math.PI;
        }
        mapbearing = Math.atan2(mapy - curr_point.y - y_disp, mapx - curr_point.x - x_disp);

        curr_prob *=
            NORMALIZATION
                * (Math.exp(
                        -0.5 * (mapbearing - fid_bearing) * (mapbearing - fid_bearing) / ROT_SIGMA)
                    / Math.sqrt(2 * Math.PI * ROT_SIGMA * ROT_SIGMA));
      }

      probsum += curr_prob;
      new_probs.add(curr_prob);
      cumulative_probs.add(probsum);
    }

    if (revFiducials.size() > 0) {
      System.out.println("Localization: localizing with fiducials");
      particles.clear();
      double rand_val;
      int index;
      int max_index = 0;
      for (int i = 0; i < NUM_PARTICLES; i++) {
        rand_val = probsum * Math.random();
        index = Collections.binarySearch(cumulative_probs, rand_val);
        if (index < 0) {
          index = -(index + 1);
        }

        particles.add(new_particles.get(index));

        if (new_probs.get(max_index) < new_probs.get(i)) {
          max_index = i;
        }
      }

      localPoint = new_particles.get(max_index);
    } else {
      System.out.println("Localization: no fiducials to localize with");
      particles = new_particles;
    }

    return localPoint;
  }