Exemple #1
0
        /// <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);
        }
Exemple #2
0
        /// <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);
        }
Exemple #3
0
        /// <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);
        }
Exemple #4
0
        /// <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);
        }
Exemple #5
0
        /// <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);
        }