public void Create()
{
int no_of_grids = 2;
int grid_type = metagrid.TYPE_SIMPLE;
int dimension_mm = 3000;
int dimension_vertical_mm = 2000;
int cellSize_mm = 32;
int localisationRadius_mm = 2000;
int maxMappingRange_mm = 2000;
float vacancyWeighting = 0.5f;
metagridBuffer buffer =
new metagridBuffer(
no_of_grids,
grid_type,
dimension_mm,
dimension_vertical_mm,
cellSize_mm,
localisationRadius_mm,
maxMappingRange_mm,
vacancyWeighting);
Assert.AreNotEqual(null, buffer);
Assert.AreNotEqual(null, buffer.buffer);
Assert.AreEqual(2, buffer.buffer.Length);
Assert.AreNotEqual(null, buffer.buffer[0]);
Assert.AreNotEqual(null, buffer.buffer[1]);
buffer.Reset();
}
public void CreateSensorModels(
metagridBuffer buf)
{
List <int> cell_sizes = buf.GetCellSizes();
sensormodel = new stereoModel[image_width.Length][];
for (int stereo_cam = 0; stereo_cam < image_width.Length; stereo_cam++)
{
sensormodel[stereo_cam] = new stereoModel[cell_sizes.Count];
}
for (int stereo_cam = 0; stereo_cam < image_width.Length; stereo_cam++)
{
for (int grid_level = 0; grid_level < cell_sizes.Count; grid_level++)
{
if (stereo_cam > 0)
{
if (image_width[stereo_cam - 1] ==
image_width[stereo_cam])
{
sensormodel[stereo_cam][grid_level] = sensormodel[stereo_cam - 1][grid_level];
}
}
if (sensormodel[stereo_cam][grid_level] == null)
{
sensormodel[stereo_cam][grid_level] = new stereoModel();
sensormodel[stereo_cam][grid_level].createLookupTable(
cell_sizes[grid_level],
image_width[stereo_cam],
image_height[stereo_cam]);
}
}
}
}
public steersman(
int body_width_mm,
int body_length_mm,
int body_height_mm,
int centre_of_rotation_x,
int centre_of_rotation_y,
int centre_of_rotation_z,
int head_centroid_x,
int head_centroid_y,
int head_centroid_z,
string sensormodels_filename,
int no_of_stereo_cameras,
float baseline_mm,
float dist_from_centre_of_tilt_mm,
int image_width,
int image_height,
float FOV_degrees,
float head_diameter_mm,
float default_head_orientation_degrees,
int no_of_grid_levels,
int dimension_mm,
int dimension_vertical_mm,
int cellSize_mm)
{
rnd = new Random(0);
int grid_type = metagrid.TYPE_SIMPLE;
int localisationRadius_mm = dimension_mm * 50 / 100;
int maxMappingRange_mm = dimension_mm * 50 / 100;
float vacancyWeighting = 0.5f;
buffer = new metagridBuffer(
no_of_grid_levels,
grid_type,
dimension_mm,
dimension_vertical_mm,
cellSize_mm,
localisationRadius_mm,
maxMappingRange_mm,
vacancyWeighting);
robot_geometry = new robotGeometry();
robot_geometry.SetBodyDimensions(body_width_mm, body_length_mm, body_height_mm);
robot_geometry.SetCentreOfRotation(centre_of_rotation_x, centre_of_rotation_y, centre_of_rotation_z);
robot_geometry.SetHeadPosition(head_centroid_x, head_centroid_y, head_centroid_z);
robot_geometry.CreateStereoCameras(
no_of_stereo_cameras,
baseline_mm,
dist_from_centre_of_tilt_mm,
image_width,
image_height,
FOV_degrees,
head_diameter_mm,
default_head_orientation_degrees);
robot_geometry.CreateSensorModels(buffer);
}
public void SaveAndLoad()
{
string filename = "tests_metagridbuffer_SaveAndLoad.xml";
int no_of_grids = 2;
int grid_type = metagrid.TYPE_SIMPLE;
int dimension_mm = 3000;
int dimension_vertical_mm = 2000;
int cellSize_mm = 32;
int localisationRadius_mm = 2000;
int maxMappingRange_mm = 2000;
float vacancyWeighting = 0.5f;
metagridBuffer buffer1 =
new metagridBuffer(
no_of_grids,
grid_type,
dimension_mm,
dimension_vertical_mm,
cellSize_mm,
localisationRadius_mm,
maxMappingRange_mm,
vacancyWeighting);
buffer1.Save(filename);
metagridBuffer buffer2 = new metagridBuffer();
buffer2.Load(filename);
Assert.AreEqual(buffer1.no_of_grid_levels, buffer2.no_of_grid_levels);
Assert.AreEqual(buffer1.grid_type, buffer2.grid_type);
Assert.AreEqual(buffer1.dimension_mm, buffer2.dimension_mm);
Assert.AreEqual(buffer1.dimension_vertical_mm, buffer2.dimension_vertical_mm);
Assert.AreEqual(buffer1.cellSize_mm, buffer2.cellSize_mm);
Assert.AreEqual(buffer1.localisationRadius_mm, buffer2.localisationRadius_mm);
Assert.AreEqual(buffer1.maxMappingRange_mm, buffer2.maxMappingRange_mm);
Assert.AreEqual(buffer1.vacancyWeighting, buffer2.vacancyWeighting);
}
public void LocaliseAlongPath()
{
// systematic bias
float bias_x_mm = -200;
float bias_y_mm = 0;
// number of stereo cameras on the robot's head
int no_of_stereo_cameras = 2;
// diameter of the robot's head
float head_diameter_mm = 100;
// number of stereo features per step during mapping
int no_of_mapping_stereo_features = 300;
// number of stereo features observed per localisation step
int no_of_localisation_stereo_features = 100;
string filename = "localise_along_path.dat";
float path_length_mm = 20000;
float start_orientation = 0;
float end_orientation = 0; // 90 * (float)Math.PI / 180.0f;
float distance_between_poses_mm = 100;
float disparity = 15;
string overall_map_filename = "overall_map.jpg";
byte[] overall_map_img = null;
int overall_img_width = 640;
int overall_img_height = 480;
int overall_map_dimension_mm = 0;
int overall_map_centre_x_mm = 0;
int overall_map_centre_y_mm = 0;
string[] str = filename.Split('.');
List <OdometryData> path = null;
SavePath(
filename,
path_length_mm,
start_orientation,
end_orientation,
distance_between_poses_mm,
disparity,
no_of_mapping_stereo_features,
no_of_stereo_cameras,
ref path);
Assert.AreEqual(true, File.Exists(filename));
Assert.AreEqual(true, File.Exists(str[0] + "_disparities_index.dat"));
Assert.AreEqual(true, File.Exists(str[0] + "_disparities.dat"));
int no_of_grids = 1;
int grid_type = metagrid.TYPE_SIMPLE;
int dimension_mm = 8000;
int dimension_vertical_mm = 2000;
int cellSize_mm = 50;
int localisationRadius_mm = 8000;
int maxMappingRange_mm = 10000;
float vacancyWeighting = 0.5f;
metagridBuffer buffer =
new metagridBuffer(
no_of_grids,
grid_type,
dimension_mm,
dimension_vertical_mm,
cellSize_mm,
localisationRadius_mm,
maxMappingRange_mm,
vacancyWeighting);
buffer.LoadPath(
filename,
str[0] + "_disparities_index.dat",
str[0] + "_disparities.dat",
ref overall_map_dimension_mm,
ref overall_map_centre_x_mm,
ref overall_map_centre_y_mm);
int img_width = 640;
int img_height = 640;
byte[] img = new byte[img_width * img_height * 3];
Bitmap bmp = new Bitmap(img_width, img_height, System.Drawing.Imaging.PixelFormat.Format24bppRgb);
buffer.ShowPath(img, img_width, img_height, true, true);
BitmapArrayConversions.updatebitmap_unsafe(img, bmp);
bmp.Save("localise_along_path.jpg", System.Drawing.Imaging.ImageFormat.Jpeg);
robotGeometry geom = new robotGeometry();
geom.CreateStereoCameras(
no_of_stereo_cameras, 120, 0,
320, 240,
65,
head_diameter_mm,
0);
geom.CreateSensorModels(buffer);
Random rnd = new Random(0);
pos3D pose_offset = null;
bool buffer_transition = false;
float[][] stereo_features = new float[no_of_stereo_cameras][];
byte[][,] stereo_features_colour = new byte[no_of_stereo_cameras][, ];
float[][] stereo_features_uncertainties = new float[no_of_stereo_cameras][];
for (int i = 0; i < no_of_stereo_cameras; i++)
{
stereo_features_uncertainties[i] = new float[no_of_localisation_stereo_features];
for (int j = 0; j < no_of_localisation_stereo_features; j++)
{
stereo_features_uncertainties[i][j] = 1;
}
}
float average_offset_x_mm = 0;
float average_offset_y_mm = 0;
List <OdometryData> estimated_path = new List <OdometryData>();
int no_of_localisation_failures = 0;
for (int i = 0; i < path.Count - 1; i += 5)
{
string debug_mapping_filename = "localise_along_path_map_" + i.ToString() + ".jpg";
OdometryData p0 = path[i];
OdometryData p1 = path[i + 1];
// create an intermediate pose
for (int cam = 0; cam < no_of_stereo_cameras; cam++)
{
geom.pose[cam].x = p0.x + ((p1.x - p0.x) / 2) + bias_x_mm;
geom.pose[cam].y = p0.y + ((p1.y - p0.y) / 2) + bias_y_mm;
geom.pose[cam].z = 0;
geom.pose[cam].pan = p0.orientation + ((p1.orientation - p0.orientation) / 2);
// create stereo features
int ctr = 0;
stereo_features[cam] = new float[no_of_localisation_stereo_features * 3];
stereo_features_colour[cam] = new byte[no_of_localisation_stereo_features, 3];
for (int f = 0; f < no_of_localisation_stereo_features; f += 5)
{
if (f < no_of_localisation_stereo_features / 2)
{
stereo_features[cam][ctr++] = 20;
stereo_features[cam][ctr++] = rnd.Next(239);
}
else
{
stereo_features[cam][ctr++] = geom.image_width[cam] - 20;
stereo_features[cam][ctr++] = rnd.Next(239);
}
stereo_features[cam][ctr++] = disparity;
}
}
float matching_score = buffer.Localise(
geom,
stereo_features,
stereo_features_colour,
stereo_features_uncertainties,
rnd,
ref pose_offset,
ref buffer_transition,
debug_mapping_filename,
bias_x_mm, bias_y_mm,
overall_map_filename,
ref overall_map_img,
overall_img_width,
overall_img_height,
overall_map_dimension_mm,
overall_map_centre_x_mm,
overall_map_centre_y_mm);
if (matching_score != occupancygridBase.NO_OCCUPANCY_EVIDENCE)
{
Console.WriteLine("pose_offset (mm): " + pose_offset.x.ToString() + ", " + pose_offset.y.ToString() + ", " + pose_offset.pan.ToString());
OdometryData estimated_pose = new OdometryData();
estimated_pose.x = geom.pose[0].x + pose_offset.x;
estimated_pose.y = geom.pose[0].y + pose_offset.y;
estimated_pose.orientation = geom.pose[0].pan + pose_offset.pan;
estimated_path.Add(estimated_pose);
average_offset_x_mm += pose_offset.x;
average_offset_y_mm += pose_offset.y;
}
else
{
// fail!
no_of_localisation_failures++;
Console.WriteLine("Localisation failure");
}
}
buffer.ShowPath(img, img_width, img_height, true, true);
BitmapArrayConversions.updatebitmap_unsafe(img, bmp);
bmp.Save("localisations_along_path.jpg", System.Drawing.Imaging.ImageFormat.Jpeg);
average_offset_x_mm /= estimated_path.Count;
average_offset_y_mm /= estimated_path.Count;
Console.WriteLine("Average offsets: " + average_offset_x_mm.ToString() + ", " + average_offset_y_mm.ToString());
float diff_x_mm = Math.Abs(average_offset_x_mm - bias_x_mm);
float diff_y_mm = Math.Abs(average_offset_y_mm - bias_y_mm);
Assert.Less(diff_x_mm, cellSize_mm * 3 / 2, "x bias not detected");
Assert.Less(diff_y_mm, cellSize_mm * 3 / 2, "y bias not detected");
if (no_of_localisation_failures > 0)
{
Console.WriteLine("Localisation failures: " + no_of_localisation_failures.ToString());
}
else
{
Console.WriteLine("No localisation failures!");
}
Assert.Less(no_of_localisation_failures, 4, "Too many localisation failures");
}
public void LoadPath()
{
string filename = "load_path.dat";
float path_length_mm = 10000;
float start_orientation = 0;
float end_orientation = 90 * (float)Math.PI / 180.0f;
float distance_between_poses_mm = 100;
float disparity = 15;
int no_of_stereo_cameras = 1;
string[] str = filename.Split('.');
List <OdometryData> path = null;
SavePath(
filename,
path_length_mm,
start_orientation,
end_orientation,
distance_between_poses_mm,
disparity,
300,
no_of_stereo_cameras,
ref path);
Assert.AreEqual(true, File.Exists(filename));
Assert.AreEqual(true, File.Exists(str[0] + "_disparities_index.dat"));
Assert.AreEqual(true, File.Exists(str[0] + "_disparities.dat"));
int no_of_grids = 2;
int grid_type = metagrid.TYPE_SIMPLE;
int dimension_mm = 3000;
int dimension_vertical_mm = 2000;
int cellSize_mm = 32;
int localisationRadius_mm = 2000;
int maxMappingRange_mm = 2000;
float vacancyWeighting = 0.5f;
metagridBuffer buffer =
new metagridBuffer(
no_of_grids,
grid_type,
dimension_mm,
dimension_vertical_mm,
cellSize_mm,
localisationRadius_mm,
maxMappingRange_mm,
vacancyWeighting);
int overall_map_dimension_mm = 0;
int overall_map_centre_x_mm = 0;
int overall_map_centre_y_mm = 0;
buffer.LoadPath(
filename,
str[0] + "_disparities_index.dat",
str[0] + "_disparities.dat",
ref overall_map_dimension_mm,
ref overall_map_centre_x_mm,
ref overall_map_centre_y_mm);
int img_width = 640;
int img_height = 480;
byte[] img = new byte[img_width * img_height * 3];
buffer.ShowPath(img, img_width, img_height, true, true);
Bitmap bmp = new Bitmap(img_width, img_height, System.Drawing.Imaging.PixelFormat.Format24bppRgb);
BitmapArrayConversions.updatebitmap_unsafe(img, bmp);
bmp.Save("load_path.jpg", System.Drawing.Imaging.ImageFormat.Jpeg);
}
/// <summary>
/// parse an xml node
/// </summary>
/// <param name="xnod"></param>
/// <param name="level"></param>
public void LoadFromXml(
XmlNode xnod, int level)
{
XmlNode xnodWorking;
if (xnod.Name == "SteersmanGeometry")
{
int cameraIndex = -1;
if (robot_geometry == null)
{
robot_geometry = new robotGeometry();
}
robot_geometry.LoadFromXml(xnod, level + 1, ref cameraIndex);
}
if (xnod.Name == "SteersmanBuffer")
{
if (buffer == null)
{
buffer = new metagridBuffer();
}
int no_of_grid_levels = 0;
int grid_type = 0;
int dimension_mm = 0;
int dimension_vertical_mm = 0;
int cellSize_mm = 0;
int localisationRadius_mm = 0;
int maxMappingRange_mm = 0;
float vacancyWeighting = 0;
buffer.LoadFromXml(xnod, level + 1,
ref no_of_grid_levels,
ref grid_type,
ref dimension_mm,
ref dimension_vertical_mm,
ref cellSize_mm,
ref localisationRadius_mm,
ref maxMappingRange_mm,
ref vacancyWeighting
);
buffer.Initialise(
no_of_grid_levels,
grid_type,
dimension_mm,
dimension_vertical_mm,
cellSize_mm,
localisationRadius_mm,
maxMappingRange_mm,
vacancyWeighting);
}
if (xnod.Name == "SteersmanSensorModels")
{
if (robot_geometry == null)
{
robot_geometry = new robotGeometry();
}
int no_of_stereo_cameras = 0;
int no_of_grid_levels = 0;
int camera_index = 0;
int grid_level = 0;
robot_geometry.LoadFromXmlSensorModels(
xnod, level + 1,
ref no_of_stereo_cameras,
ref no_of_grid_levels,
ref camera_index,
ref grid_level);
}
// call recursively on all children of the current node
if (xnod.HasChildNodes)
{
xnodWorking = xnod.FirstChild;
while (xnodWorking != null)
{
LoadFromXml(xnodWorking, level + 1);
xnodWorking = xnodWorking.NextSibling;
}
}
}
