public void ReceivedOdometryUpdate(ROSBridgeMsg data)
{
//In WGS84
OdometryMsg nav = (OdometryMsg)data;
GeoPointWGS84 geoPoint = new GeoPointWGS84
{
latitude = nav._pose._pose._position.GetY(),
longitude = nav._pose._pose._position.GetX(),
altitude = nav._pose._pose._position.GetZ(),
};
Quaternion orientation = new Quaternion(
x: nav._pose._pose._orientation.GetX(),
z: nav._pose._pose._orientation.GetY(),
y: nav._pose._pose._orientation.GetZ(),
w: nav._pose._pose._orientation.GetW()
);
_odometryDataToConsume = new OdometryData
{
Position = geoPoint,
Orientation = orientation
};
_hasOdometryDataToConsume = true;
}
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");
}
/// <summary>
/// saves path data to file
/// </summary>
/// <param name="filename">filename to save as</param>
/// <param name="path_length_mm">length of the path</param>
/// <param name="start_orientation">orientation at the start of the path</param>
/// <param name="end_orientation">orientation at the end of the path</param>
/// <param name="distance_between_poses_mm">distance between poses</param>
public static void SavePath(
string filename,
float path_length_mm,
float start_orientation,
float end_orientation,
float distance_between_poses_mm,
float disparity,
int no_of_stereo_features,
int no_of_stereo_cameras,
ref List <OdometryData> save_path)
{
string[] str = filename.Split('.');
float start_x_mm = 0;
float start_y_mm = 0;
float x_mm = start_x_mm, y_mm = start_y_mm;
float orientation = start_orientation;
int steps = (int)(path_length_mm / distance_between_poses_mm);
save_path = new List <OdometryData>();
Random rnd = new Random(0);
if (File.Exists(str[0] + "_disparities_index.dat"))
{
File.Delete(str[0] + "_disparities_index.dat");
}
if (File.Exists(str[0] + "_disparities.dat"))
{
File.Delete(str[0] + "_disparities.dat");
}
for (int i = 0; i < steps; i++)
{
for (int cam = 0; cam < no_of_stereo_cameras; cam++)
{
OdometryData data = new OdometryData();
data.orientation = orientation;
data.x = x_mm;
data.y = y_mm;
save_path.Add(data);
List <StereoFeatureTest> features = new List <StereoFeatureTest>();
for (int f = 0; f < no_of_stereo_features; f++)
{
StereoFeatureTest feat;
if (f < no_of_stereo_features / 2)
{
feat = new StereoFeatureTest(20, rnd.Next(239), disparity);
}
else
{
feat = new StereoFeatureTest(300, rnd.Next(239), disparity);
}
feat.SetColour(0, 0, 0);
features.Add(feat);
}
ProcessPose(
str[0],
DateTime.Now,
x_mm, y_mm,
orientation,
0, 0, 0,
cam, features);
}
x_mm += distance_between_poses_mm * (float)Math.Sin(orientation);
y_mm += distance_between_poses_mm * (float)Math.Cos(orientation);
orientation = start_orientation + ((end_orientation - start_orientation) * i / steps);
}
FileStream fs = File.Open(filename, FileMode.Create);
BinaryWriter bw = new BinaryWriter(fs);
bw.Write(save_path.Count);
for (int i = 0; i < save_path.Count; i++)
{
save_path[i].Write(bw);
}
bw.Close();
fs.Close();
// save images of the path
if (filename.Contains("."))
{
int img_width = 640;
int img_height = 480;
byte[] img = new byte[img_width * img_height * 3];
Bitmap bmp = new Bitmap(img_width, img_height, System.Drawing.Imaging.PixelFormat.Format24bppRgb);
filename = str[0] + "_positions.jpg";
float tx = 0, ty = 0;
float bx = 0, by = 0;
ShowPath(
save_path, img, img_width, img_height,
0, 0, 0, true,
ref tx, ref ty,
ref bx, ref by);
BitmapArrayConversions.updatebitmap_unsafe(img, bmp);
bmp.Save(filename, System.Drawing.Imaging.ImageFormat.Jpeg);
}
}
public void LocaliseAlongPath()
{
// systematic bias
float bias_x_mm = -200;
float bias_y_mm = 0;
// 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 = 50;
string filename = "steersman_localise_along_path.dat";
float path_length_mm = 10000;
float start_orientation = 0;
float end_orientation = 0; //90 * (float)Math.PI / 180.0f;
float distance_between_poses_mm = 100;
float disparity = 15;
string steersman_filename = "tests_steersman_LocaliseAlongPath.xml";
int body_width_mm = 465;
int body_length_mm = 380;
int body_height_mm = 1660;
int centre_of_rotation_x = body_width_mm / 2;
int centre_of_rotation_y = body_length_mm / 2;
int centre_of_rotation_z = 10;
int head_centroid_x = body_width_mm / 2;
int head_centroid_y = 65;
int head_centroid_z = 1600;
string sensormodels_filename = "";
int no_of_stereo_cameras = 2;
float baseline_mm = 120;
float dist_to_centre_of_tilt_mm = 0;
int image_width = 320;
int image_height = 240;
float FOV_degrees = 65;
float head_diameter_mm = 160;
float default_head_orientation_degrees = 0;
int no_of_grid_levels = 1;
int dimension_mm = 8000;
int dimension_vertical_mm = 2000;
int cellSize_mm = 50;
string[] str = filename.Split('.');
List <OdometryData> path = null;
tests_metagridbuffer.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"));
steersman visual_guidance = null;
if (File.Exists(steersman_filename))
{
visual_guidance = new steersman();
visual_guidance.Load(steersman_filename);
}
else
{
visual_guidance = new steersman(
body_width_mm,
body_length_mm,
body_height_mm,
centre_of_rotation_x,
centre_of_rotation_y,
centre_of_rotation_z,
head_centroid_x,
head_centroid_y,
head_centroid_z,
sensormodels_filename,
no_of_stereo_cameras,
baseline_mm,
dist_to_centre_of_tilt_mm,
image_width,
image_height,
FOV_degrees,
head_diameter_mm,
default_head_orientation_degrees,
no_of_grid_levels,
dimension_mm,
dimension_vertical_mm,
cellSize_mm);
visual_guidance.Save(steersman_filename);
}
visual_guidance.LoadPath(filename, str[0] + "_disparities_index.dat", str[0] + "_disparities.dat");
visual_guidance.ShowLocalisations("steersman_localise_along_path.jpg", 640, 480);
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;
DateTime start_time = DateTime.Now;
int no_of_localisations = 0;
for (int i = 0; i < path.Count - 1; i += 5, no_of_localisations++)
{
string debug_mapping_filename = "steersman_localise_along_path_map_" + i.ToString() + ".jpg";
OdometryData p0 = path[i];
OdometryData p1 = path[i + 1];
float current_x_mm = p0.x + ((p1.x - p0.x) / 2) + bias_x_mm;
float current_y_mm = p0.y + ((p1.y - p0.y) / 2) + bias_y_mm;
float current_pan = p0.orientation + ((p1.orientation - p0.orientation) / 2);
// create an intermediate pose
for (int cam = 0; cam < no_of_stereo_cameras; cam++)
{
// set the current pose
visual_guidance.SetCurrentPosition(
cam,
current_x_mm,
current_y_mm,
0,
current_pan,
0, 0);
// 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++] = image_width - 20;
stereo_features[cam][ctr++] = rnd.Next(239);
}
stereo_features[cam][ctr++] = disparity;
}
}
float offset_x_mm = 0;
float offset_y_mm = 0;
float offset_z_mm = 0;
float offset_pan = 0;
float offset_tilt = 0;
float offset_roll = 0;
bool valid_localisation = visual_guidance.Localise(
stereo_features,
stereo_features_colour,
stereo_features_uncertainties,
debug_mapping_filename,
bias_x_mm, bias_y_mm,
ref offset_x_mm,
ref offset_y_mm,
ref offset_z_mm,
ref offset_pan,
ref offset_tilt,
ref offset_roll);
if (valid_localisation)
{
Console.WriteLine("pose_offset (mm): " + offset_x_mm.ToString() + ", " + offset_y_mm.ToString() + ", " + offset_pan.ToString());
OdometryData estimated_pose = new OdometryData();
estimated_pose.x = current_x_mm + offset_x_mm;
estimated_pose.y = current_y_mm + offset_y_mm;
estimated_pose.orientation = current_pan + offset_pan;
estimated_path.Add(estimated_pose);
average_offset_x_mm += offset_x_mm;
average_offset_y_mm += offset_y_mm;
}
else
{
// fail!
no_of_localisation_failures++;
Console.WriteLine("Localisation failure");
}
}
TimeSpan diff = DateTime.Now.Subtract(start_time);
float time_per_localisation_sec = (float)diff.TotalSeconds / no_of_localisations;
Console.WriteLine("Time per localisation: " + time_per_localisation_sec.ToString() + " sec");
visual_guidance.ShowLocalisations("steersman_localisations_along_path.jpg", 640, 480);
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");
}