/// <summary>
/// returns a version of this evidence ray rotated through the given
/// pan angle in the XY plane. This is used for generating trial poses.
/// </summary>
/// <param name="extra_pan">pan angle to be added</param>
/// <param name="translation_x">new obsevation x coordinate</param>
/// <param name="translation_y">new obsevation y coordinate</param>
/// <returns>evidence ray object</returns>
public evidenceRay trialPose(
float extra_pan,
float extra_tilt,
float extra_roll,
float translation_x,
float translation_y,
float translation_z)
{
evidenceRay rotated_ray = new evidenceRay();
float new_pan_angle = extra_pan + pan_angle;
float new_tilt_angle = extra_tilt + tilt_angle;
float new_roll_angle = extra_roll + roll_angle;
// as observed from the centre of the camera baseline
rotated_ray.observedFrom = new pos3D(translation_x, translation_y, translation_z);
rotated_ray.fattestPoint = fattestPoint;
pos3D r1 = new pos3D(0, start_dist, 0);
rotated_ray.vertices[0] = r1.rotate(new_pan_angle, new_tilt_angle, new_roll_angle);
rotated_ray.vertices[0].x += translation_x;
rotated_ray.vertices[0].y += translation_y;
rotated_ray.vertices[0].z += translation_z;
pos3D r2 = new pos3D(0, start_dist + length, 0);
rotated_ray.vertices[1] = r2.rotate(new_pan_angle, new_tilt_angle, new_roll_angle);
rotated_ray.vertices[1].x += translation_x;
rotated_ray.vertices[1].y += translation_y;
rotated_ray.vertices[1].z += translation_z;
rotated_ray.pan_angle = new_pan_angle;
rotated_ray.tilt_angle = new_tilt_angle;
rotated_ray.roll_angle = new_roll_angle;
rotated_ray.pan_index = (int)(new_pan_angle * pan_steps / 6.2831854f);
rotated_ray.tilt_index = (int)(new_tilt_angle * pan_steps / 6.2831854f);
rotated_ray.roll_index = (int)(new_roll_angle * pan_steps / 6.2831854f);
rotated_ray.length = length;
rotated_ray.width = width;
rotated_ray.start_dist = start_dist;
rotated_ray.disparity = disparity;
for (int col = 2; col >= 0; col--)
{
rotated_ray.colour[col] = colour[col];
}
return(rotated_ray);
}
/// <summary>
/// add an observation taken from this pose
/// </summary>
/// <param name="stereo_rays">list of ray objects in this observation</param>
/// <param name="rob">robot object</param>
/// <param name="LocalGrid">occupancy grid into which to insert the observation</param>
/// <param name="localiseOnly">if true does not add any mapping particles (pure localisation)</param>
/// <returns>localisation matching score</returns>
public float AddObservation(
List <evidenceRay>[] stereo_rays,
robot rob,
dpslam LocalGrid,
bool localiseOnly)
{
// clear the localisation score
float localisation_score = occupancygridCellMultiHypothesis.NO_OCCUPANCY_EVIDENCE;
// get the positions of the head and cameras for this pose
pos3D head_location = new pos3D(0, 0, 0);
pos3D[] camera_centre_location = new pos3D[rob.head.no_of_stereo_cameras];
pos3D[] left_camera_location = new pos3D[rob.head.no_of_stereo_cameras];
pos3D[] right_camera_location = new pos3D[rob.head.no_of_stereo_cameras];
calculateCameraPositions(
rob,
ref head_location,
ref camera_centre_location,
ref left_camera_location,
ref right_camera_location);
// itterate for each stereo camera
int cam = stereo_rays.Length - 1;
while (cam >= 0)
{
// itterate through each ray
int r = stereo_rays[cam].Count - 1;
while (r >= 0)
{
// observed ray. Note that this is in an egocentric
// coordinate frame relative to the head of the robot
evidenceRay ray = stereo_rays[cam][r];
// translate and rotate this ray appropriately for the pose
evidenceRay trial_ray =
ray.trialPose(
camera_centre_location[cam].pan,
camera_centre_location[cam].tilt,
camera_centre_location[cam].roll,
camera_centre_location[cam].x,
camera_centre_location[cam].y,
camera_centre_location[cam].z);
//Console.WriteLine("ray.vert[0] " + (trial_ray.vertices[0] == null).ToString());
//Console.WriteLine("ray.vert[1] " + (trial_ray.vertices[1] == null).ToString());
// update the grid cells for this ray and update the
// localisation score accordingly
float score =
LocalGrid.Insert(
trial_ray, this,
rob.head.sensormodel[cam],
left_camera_location[cam],
right_camera_location[cam],
localiseOnly);
if (score != occupancygridCellMultiHypothesis.NO_OCCUPANCY_EVIDENCE)
{
if (localisation_score != occupancygridCellMultiHypothesis.NO_OCCUPANCY_EVIDENCE)
{
localisation_score += score;
}
else
{
localisation_score = score;
}
}
r--;
}
cam--;
}
return(localisation_score);
}
/// <summary>
/// creates a ray object which may be used to update occupancy grids
/// </summary>
/// <param name="x">x position of the feature within the camera image</param>
/// <param name="y">y position of the feature within the camera image</param>
/// <param name="disparity">stereo disparity in pixels</param>
/// <param name="uncertainty">standard deviation</param>
/// <param name="r">red colour component of the ray</param>
/// <param name="g">green colour component of the ray</param>
/// <param name="b">blue colour component of the ray</param>
/// <returns>evidence ray object</returns>
public evidenceRay createRay(
float x, float y, float disparity,
float uncertainty,
byte r, byte g, byte b)
{
evidenceRay ray = null;
float x1 = x + disparity;
float x2 = x;
float x_start = 0, y_start = 0;
float x_end = 0, y_end = 0;
float x_left = 0, y_left = 0;
float x_right = 0, y_right = 0;
int grid_dimension = 2000;
float distance = 100; //DisparityToDistance(disparity, focal_length, sensor_pixels_per_mm, baseline);
raysIntersection(x1, x2, grid_dimension, uncertainty, distance,
ref x_start, ref y_start, ref x_end, ref y_end,
ref x_left, ref y_left, ref x_right, ref y_right);
if (y_start < -1) y_start = -y_start;
if (y_end < -1) y_end = -y_end;
if (x_right >= x_left)
{
// calc uncertainty in angle (+/- half a pixel)
//float angular_uncertainty = FOV_vertical / (image_height * 2);
// convert y image position to z height
int half_height = image_height / 2;
float angle = ((y - half_height) * FOV_vertical / image_height);
//float z = (int)(y_left * Math.Sin(angle));
float z_start = (int)(y_start * Math.Sin(angle));
float z_end = (int)(y_end * Math.Sin(angle));
//use an offset so that the point from which the ray was observed was (0,0,0)
float x_offset = grid_dimension * 0.5f;
ray = new evidenceRay();
ray.vertices[0] = new pos3D(x_start - x_offset, y_start, z_start);
ray.vertices[1] = new pos3D(x_end - x_offset, y_end, z_end);
ray.fattestPoint = (y_left - y_start) / (y_end - y_start);
//ray.vertices[2] = new pos3D(x_left - x_offset, y_left, z);
//ray.vertices[3] = new pos3D(x_right - x_offset, y_right, z);
//ray.centre = new pos3D(x_left + ((x_right-x_left)*0.5f) - x_offset, y_left, z);
ray.width = x_right - x_left;
ray.colour[0] = r;
ray.colour[1] = g;
ray.colour[2] = b;
ray.uncertainty = uncertainty;
ray.disparity = disparity;
ray.sigma = sigma;
}
return (ray);
}
/// <summary>
/// inserts the given ray into the grid
/// There are three components to the sensor model used here:
/// two areas determining the probably vacant area and one for
/// the probably occupied space
/// </summary>
/// <param name="ray">ray object to be inserted into the grid</param>
/// <param name="origin">the pose of the robot</param>
/// <param name="sensormodel">the sensor model to be used</param>
/// <param name="leftcam_x">x position of the left camera in millimetres</param>
/// <param name="leftcam_y">y position of the left camera in millimetres</param>
/// <param name="rightcam_x">x position of the right camera in millimetres</param>
/// <param name="rightcam_y">y position of the right camera in millimetres</param>
/// <param name="localiseOnly">if true does not add any mapping particles (pure localisation)</param>
/// <returns>matching probability, expressed as log odds</returns>
public float Insert(
evidenceRay ray,
particlePose origin,
rayModelLookup sensormodel_lookup,
pos3D left_camera_location,
pos3D right_camera_location,
bool localiseOnly)
{
// some constants to aid readability
const int OCCUPIED_SENSORMODEL = 0;
const int VACANT_SENSORMODEL_LEFT_CAMERA = 1;
const int VACANT_SENSORMODEL_RIGHT_CAMERA = 2;
const int X_AXIS = 0;
const int Y_AXIS = 1;
// which disparity index in the lookup table to use
// we multiply by 2 because the lookup is in half pixel steps
float ray_model_interval_pixels = sensormodel_lookup.ray_model_interval_pixels;
int sensormodel_index = (int)Math.Round(ray.disparity / ray_model_interval_pixels);
// the initial models are blank, so just default to the one disparity pixel model
bool small_disparity_value = false;
if (sensormodel_index < 2)
{
sensormodel_index = 2;
small_disparity_value = true;
}
// beyond a certain disparity the ray model for occupied space
// is always only going to be only a single grid cell
if (sensormodel_lookup == null) Console.WriteLine("Sensor models are missing");
if (sensormodel_index >= sensormodel_lookup.probability.GetLength(0))
sensormodel_index = sensormodel_lookup.probability.GetLength(0) - 1;
float xdist_mm=0, ydist_mm=0, zdist_mm=0, xx_mm=0, yy_mm=0, zz_mm=0;
float occupied_dx = 0, occupied_dy = 0, occupied_dz = 0;
float intersect_x = 0, intersect_y = 0, intersect_z = 0;
float centre_prob = 0, prob = 0, prob_localisation = 0; // probability values at the centre axis and outside
float matchingScore = occupancygridCellMultiHypothesis.NO_OCCUPANCY_EVIDENCE; // total localisation matching score
int rayWidth = 0; // widest point in the ray in cells
int widest_point; // widest point index
int step_size = 1;
particleGridCell hypothesis;
// ray width at the fattest point in cells
rayWidth = (int)Math.Round(ray.width / (cellSize_mm * 2));
// calculate the centre position of the grid in millimetres
int half_grid_width_mm = dimension_cells * cellSize_mm / 2;
//int half_grid_width_vertical_mm = dimension_cells_vertical * cellSize_mm / 2;
int grid_centre_x_mm = (int)(x - half_grid_width_mm);
int grid_centre_y_mm = (int)(y - half_grid_width_mm);
int grid_centre_z_mm = (int)z;
//int max_dimension_cells = dimension_cells - rayWidth;
// in turbo mode only use a single vacancy ray
int max_modelcomponent = VACANT_SENSORMODEL_RIGHT_CAMERA;
if (TurboMode) max_modelcomponent = VACANT_SENSORMODEL_LEFT_CAMERA;
float[][] sensormodel_lookup_probability = sensormodel_lookup.probability;
// consider each of the three parts of the sensor model
for (int modelcomponent = OCCUPIED_SENSORMODEL; modelcomponent <= max_modelcomponent; modelcomponent++)
{
// the range from the cameras from which insertion of data begins
// for vacancy rays this will be zero, but will be non-zero for the occupancy area
int starting_range_cells = 0;
switch (modelcomponent)
{
case OCCUPIED_SENSORMODEL:
{
// distance between the beginning and end of the probably
// occupied area
occupied_dx = ray.vertices[1].x - ray.vertices[0].x;
occupied_dy = ray.vertices[1].y - ray.vertices[0].y;
occupied_dz = ray.vertices[1].z - ray.vertices[0].z;
intersect_x = ray.vertices[0].x + (occupied_dx * ray.fattestPoint);
intersect_y = ray.vertices[0].y + (occupied_dy * ray.fattestPoint);
intersect_z = ray.vertices[0].z + (occupied_dz * ray.fattestPoint);
xdist_mm = occupied_dx;
ydist_mm = occupied_dy;
zdist_mm = occupied_dz;
// begin insertion at the beginning of the
// probably occupied area
xx_mm = ray.vertices[0].x;
yy_mm = ray.vertices[0].y;
zz_mm = ray.vertices[0].z;
break;
}
case VACANT_SENSORMODEL_LEFT_CAMERA:
{
// distance between the left camera and the left side of
// the probably occupied area of the sensor model
xdist_mm = intersect_x - left_camera_location.x;
ydist_mm = intersect_y - left_camera_location.y;
zdist_mm = intersect_z - left_camera_location.z;
// begin insertion from the left camera position
xx_mm = left_camera_location.x;
yy_mm = left_camera_location.y;
zz_mm = left_camera_location.z;
step_size = 2;
break;
}
case VACANT_SENSORMODEL_RIGHT_CAMERA:
{
// distance between the right camera and the right side of
// the probably occupied area of the sensor model
xdist_mm = intersect_x - right_camera_location.x;
ydist_mm = intersect_y - right_camera_location.y;
zdist_mm = intersect_z - right_camera_location.z;
// begin insertion from the right camera position
xx_mm = right_camera_location.x;
yy_mm = right_camera_location.y;
zz_mm = right_camera_location.z;
step_size = 2;
break;
}
}
// which is the longest axis ?
int longest_axis = X_AXIS;
float longest = Math.Abs(xdist_mm);
if (Math.Abs(ydist_mm) > longest)
{
// y has the longest length
longest = Math.Abs(ydist_mm);
longest_axis = Y_AXIS;
}
// ensure that the vacancy model does not overlap
// the probably occupied area
// This is crude and could potentially leave a gap
if (modelcomponent != OCCUPIED_SENSORMODEL)
longest -= ray.width;
int steps = (int)(longest / cellSize_mm);
if (steps < 1) steps = 1;
// calculate the range from the cameras to the start of the ray in grid cells
if (modelcomponent == OCCUPIED_SENSORMODEL)
{
if (longest_axis == Y_AXIS)
starting_range_cells = (int)Math.Abs((ray.vertices[0].y - ray.observedFrom.y) / cellSize_mm);
else
starting_range_cells = (int)Math.Abs((ray.vertices[0].x - ray.observedFrom.x) / cellSize_mm);
}
// what is the widest point of the ray in cells
if (modelcomponent == OCCUPIED_SENSORMODEL)
widest_point = (int)(ray.fattestPoint * steps / ray.length);
else
widest_point = steps;
// calculate increment values in millimetres
float x_incr_mm = xdist_mm / steps;
float y_incr_mm = ydist_mm / steps;
float z_incr_mm = zdist_mm / steps;
// step through the ray, one grid cell at a time
int grid_step = 0;
while (grid_step < steps)
{
// is this position inside the maximum mapping range
bool withinMappingRange = true;
if (grid_step + starting_range_cells > max_mapping_range_cells)
{
withinMappingRange = false;
if ((grid_step==0) && (modelcomponent == OCCUPIED_SENSORMODEL))
{
grid_step = steps;
modelcomponent = 9999;
}
}
// calculate the width of the ray in cells at this point
// using a diamond shape ray model
int ray_wdth = 0;
if (rayWidth > 0)
{
if (grid_step < widest_point)
ray_wdth = grid_step * rayWidth / widest_point;
else
{
if (!small_disparity_value)
// most disparity values tail off to some point in the distance
ray_wdth = (steps - grid_step + widest_point) * rayWidth / (steps - widest_point);
else
// for very small disparity values the ray model has an infinite tail
// and maintains its width after the widest point
ray_wdth = rayWidth;
}
}
// localisation rays are wider, to enable a more effective matching score
// which is not too narrowly focussed and brittle
int ray_wdth_localisation = ray_wdth + 1; //localisation_search_cells;
xx_mm += x_incr_mm*step_size;
yy_mm += y_incr_mm*step_size;
zz_mm += z_incr_mm*step_size;
// convert the x millimetre position into a grid cell position
int x_cell = (int)Math.Round((xx_mm - grid_centre_x_mm) / (float)cellSize_mm);
if ((x_cell > ray_wdth_localisation) && (x_cell < dimension_cells - ray_wdth_localisation))
{
// convert the y millimetre position into a grid cell position
int y_cell = (int)Math.Round((yy_mm - grid_centre_y_mm) / (float)cellSize_mm);
if ((y_cell > ray_wdth_localisation) && (y_cell < dimension_cells - ray_wdth_localisation))
{
// convert the z millimetre position into a grid cell position
int z_cell = (int)Math.Round((zz_mm - grid_centre_z_mm) / (float)cellSize_mm);
if ((z_cell >= 0) && (z_cell < dimension_cells_vertical))
{
int x_cell2 = x_cell;
int y_cell2 = y_cell;
// get the probability at this point
// for the central axis of the ray using the inverse sensor model
if (modelcomponent == OCCUPIED_SENSORMODEL)
centre_prob = ray_model_interval_pixels + (sensormodel_lookup_probability[sensormodel_index][grid_step] * ray_model_interval_pixels);
else
// calculate the probability from the vacancy model
centre_prob = vacancyFunction(grid_step / (float)steps, steps);
// width of the localisation ray
for (int width = -ray_wdth_localisation; width <= ray_wdth_localisation; width++)
{
// is the width currently inside the mapping area of the ray ?
bool isInsideMappingRayWidth = false;
if ((width >= -ray_wdth) && (width <= ray_wdth))
isInsideMappingRayWidth = true;
// adjust the x or y cell position depending upon the
// deviation from the main axis of the ray
if (longest_axis == Y_AXIS)
x_cell2 = x_cell + width;
else
y_cell2 = y_cell + width;
// probability at the central axis
prob = centre_prob;
prob_localisation = centre_prob;
// probabilities are symmetrical about the axis of the ray
// this multiplier implements a gaussian distribution around the centre
if (width != 0) // don't bother if this is the centre of the ray
{
// the probability used for wide localisation rays
prob_localisation *= gaussianLookup[Math.Abs(width) * 9 / ray_wdth_localisation];
// the probability used for narrower mapping rays
if (isInsideMappingRayWidth)
prob *= gaussianLookup[Math.Abs(width) * 9 / ray_wdth];
}
if ((cell[x_cell2][y_cell2] != null) && (withinMappingRange))
{
// only localise using occupancy, not vacancy
if (modelcomponent == OCCUPIED_SENSORMODEL)
{
// update the matching score, by combining the probability
// of the grid cell with the probability from the localisation ray
float score = occupancygridCellMultiHypothesis.NO_OCCUPANCY_EVIDENCE;
if (longest_axis == X_AXIS)
score = matchingProbability(x_cell2, y_cell2, z_cell, origin, prob_localisation, ray.colour);
if (longest_axis == Y_AXIS)
score = matchingProbability(x_cell2, y_cell2, z_cell, origin, prob_localisation, ray.colour);
if (score != occupancygridCellMultiHypothesis.NO_OCCUPANCY_EVIDENCE)
{
if (matchingScore != occupancygridCellMultiHypothesis.NO_OCCUPANCY_EVIDENCE)
matchingScore += score;
else
matchingScore = score;
}
}
}
if ((isInsideMappingRayWidth) &&
(withinMappingRange) &&
(!localiseOnly))
{
// add a new hypothesis to this grid coordinate
// note that this is also added to the original pose
hypothesis = new particleGridCell(x_cell2, y_cell2, z_cell,
prob, origin,
ray.colour);
if (origin.AddHypothesis(hypothesis, max_mapping_range_cells, dimension_cells, dimension_cells_vertical))
{
// generate a grid cell if necessary
if (cell[x_cell2][y_cell2] == null)
cell[x_cell2][y_cell2] = new occupancygridCellMultiHypothesis(dimension_cells_vertical);
cell[x_cell2][y_cell2].AddHypothesis(hypothesis);
total_valid_hypotheses++;
}
}
}
}
else grid_step = steps; // its the end of the ray, break out of the loop
}
else grid_step = steps; // its the end of the ray, break out of the loop
}
else grid_step = steps; // time to bail out chaps!
grid_step += step_size;
}
}
return (matchingScore);
}
/// <summary>
/// returns a version of this evidence ray rotated through the given
/// pan angle in the XY plane. This is used for generating trial poses.
/// </summary>
/// <param name="extra_pan">pan angle to be added</param>
/// <param name="translation_x">new obsevation x coordinate</param>
/// <param name="translation_y">new obsevation y coordinate</param>
/// <returns>evidence ray object</returns>
public evidenceRay trialPose(
float extra_pan,
float extra_tilt,
float extra_roll,
float translation_x,
float translation_y,
float translation_z)
{
evidenceRay rotated_ray = new evidenceRay();
float new_pan_angle = extra_pan + pan_angle;
float new_tilt_angle = extra_tilt + tilt_angle;
float new_roll_angle = extra_roll + roll_angle;
// as observed from the centre of the camera baseline
rotated_ray.observedFrom = new pos3D(translation_x, translation_y, translation_z);
rotated_ray.fattestPoint = fattestPoint;
pos3D r1 = new pos3D(0, start_dist, 0);
rotated_ray.vertices[0] = r1.rotate(new_pan_angle, new_tilt_angle, new_roll_angle);
rotated_ray.vertices[0].x += translation_x;
rotated_ray.vertices[0].y += translation_y;
rotated_ray.vertices[0].z += translation_z;
pos3D r2 = new pos3D(0, start_dist+length, 0);
rotated_ray.vertices[1] = r2.rotate(new_pan_angle, new_tilt_angle, new_roll_angle);
rotated_ray.vertices[1].x += translation_x;
rotated_ray.vertices[1].y += translation_y;
rotated_ray.vertices[1].z += translation_z;
rotated_ray.pan_angle = new_pan_angle;
rotated_ray.tilt_angle = new_tilt_angle;
rotated_ray.roll_angle = new_roll_angle;
rotated_ray.pan_index = (int)(new_pan_angle * pan_steps / 6.2831854f);
rotated_ray.tilt_index = (int)(new_tilt_angle * pan_steps / 6.2831854f);
rotated_ray.roll_index = (int)(new_roll_angle * pan_steps / 6.2831854f);
rotated_ray.length = length;
rotated_ray.width = width;
rotated_ray.start_dist = start_dist;
rotated_ray.disparity = disparity;
for (int col = 2; col >= 0; col--)
rotated_ray.colour[col] = colour[col];
return (rotated_ray);
}
