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Lidar Localization with Unreal Engine Simulation

This example shows how to develop and evaluate a lidar localization algorithm using synthetic lidar data from the Unreal Engine® simulation environment.

Developing a localization algorithm and evaluating its performance in varying conditions is a challenging task. One of the biggest challenges is obtaining ground truth. Although you can capture ground truth using expensive, high-precision inertial navigation systems (INS), virtual simulation is a cost-effective alternative. The use of simulation enables testing under a variety of scenarios and sensor configurations. It also enables a rapid development iteration, and provides precise ground truth.

This example uses the Unreal Engine simulation environment from Epic Games® to develop and evaluate a lidar localization algorithm from a known initial pose in a parking scenario.

Set Up Scenario in Simulation Environment

Parking a vehicle into a parking spot is a challenging maneuver that relies on accurate localization. Use the prebuilt Large Parking Lot scene to create such a scenario. The Select Waypoints for Unreal Engine Simulation example describes how to interactively select a sequence of waypoints from a scene and how to generate a reference vehicle trajectory. This example uses a recorded reference trajectory obtained using the approach described in the linked example. First, visualize the reference path on a 2-D bird's-eye view of the scene.

% Load reference path
data = load('ReferencePathForward.mat');

refPosesX = data.ReferencePathForward.refPosesX;
refPosesY = data.ReferencePathForward.refPosesY;
refPosesT = data.ReferencePathForward.refPosesT;

sceneName = 'LargeParkingLot';
hScene = figure;
helperShowSceneImage(sceneName);
hold on
scatter(refPosesX(:,2), refPosesY(:,2), [], 'filled', 'DisplayName', ...
    'Reference Path');
xlim([0 40])
ylim([-20 10])
legend
hold off

Record and Visualize Sensor Data

Set up a simple model with a hatchback vehicle moving along the specified reference path by using the Simulation 3D Vehicle with Ground Following block. Mount a lidar on the roof center of a vehicle using the Simulation 3D Lidar block. Record and visualize the sensor data. The recorded data is used to develop a localization algorithm.

close(hScene)

if ~ispc
    error("Unreal Engine Simulation is supported only on Microsoft" ...
        + char(174) + " Windows" + char(174) + ".");
end

% Open model
modelName = 'recordAndVisualize';
open_system(modelName);
snapnow;

% Run simulation
simOut = sim(modelName);

The recorded sensor data is returned in the simOut variable.

Develop Algorithm Using Recorded Data

In this example, you develop an algorithm based on point cloud registration. Point cloud registration is a common localization technique that estimates the relative motion between two point clouds to derive localization data. Accumulating relative motion like this over long sequences can lead to drift, which can be corrected using loop closure detection and pose graph optimization, as shown in the Build a Map from Lidar Data Using SLAM example. Since this example uses a short reference path, loop closure detection is omitted.

Extract the lidar sensor data and ground truth location and orientation provided by the Simulation 3D Lidar block. The ground truth location and orientation are provided in the world (scene) coordinate system. Extract the known initial pose from the ground truth data by using the helperPoseToRigidTransform function.

close_system(modelName);

% Extract lidar sensor data
ptCloudArr = helperGetPointClouds(simOut);

% Extract ground truth
[lidarLocation, lidarOrientation, simTimes] = helperGetLocalizerGroundTruth(simOut);

% Compute initial pose
initPose = [lidarLocation(1, :) lidarOrientation(1, :)];
initTform = helperPoseToRigidTransform(initPose);

Develop a lidar localization algorithm by using the extracted sensor data. Use a pcviewset (Computer Vision Toolbox) object to process and store odometry data. pcviewset organizes odometry data into a set of views, and the associated connections between views, where:

  • Each view has an absolute pose describing the rigid transformation to some fixed reference frame.

  • Each connection has a relative pose describing the rigid transformation between the two connecting views.

The localization estimate is maintained in the form of the absolute poses for each view with respect to the scene reference frame.

Use a pcplayer (Computer Vision Toolbox) object to display streaming point cloud data in the loop as it is registered. Transform the viewing angle to a top view. The orange cuboid and path show the localization position estimated by the algorithm. The green path shows the ground truth.

% Create a view set
vSet = pcviewset;

absPose = initTform;
relPose = rigid3d;
viewId  = 1;

% Define rigid transformation between the lidar sensor mounting position
% and the vehicle reference point.
lidarToVehicleTform = helperPoseToRigidTransform(single([0 0 -1.57 0 0 0]));

% Process the point cloud frame
ptCloud = helperProcessPointCloud(ptCloudArr(1));

% Initialize accumulated point cloud map
ptCloudAccum = pctransform(ptCloud, absPose);

% Add first view to the view set
vSet = addView(vSet, viewId, absPose, 'PointCloud', ptCloud);

% Configure display
xlimits = [  0  50];
ylimits = [-25  10];
zlimits = [-30  30];
player = pcplayer(xlimits, ylimits, zlimits);
estimatePathHandle = [];
truthPathHandle    = [];

% Specify vehicle dimensions
centerToFront = 1.104;
centerToRear  = 1.343;
frontOverhang = 0.828;
rearOverhang  = 0.589;
vehicleWidth  = 1.653;
vehicleHeight = 1.513;
vehicleLength = centerToFront + centerToRear + frontOverhang + rearOverhang;
hatchbackDims = vehicleDimensions(vehicleLength,vehicleWidth,vehicleHeight, ...
'FrontOverhang',frontOverhang,'RearOverhang',rearOverhang);

vehicleDims   = [hatchbackDims.Length, hatchbackDims.Width, hatchbackDims.Height];
vehicleColor  = [0.85 0.325 0.098];

% Initialize parameters
skipFrames    = 5;      % Number of frames to skip to accumulate sufficient motion
prevViewId    = viewId;
prevPtCloud   = ptCloud;

% Loop over lidar sensor frames and localize
for viewId = 6 : skipFrames : numel(ptCloudArr)
    % Process frame
    ptCloud = helperProcessPointCloud(ptCloudArr(viewId));

    % Register current frame to previous frame
    relPose = pcregistericp(ptCloud, prevPtCloud, 'MaxIterations', 40, ...
        'Metric', 'pointToPlane');

    % Since motion is restricted to a 2-D plane, discard motion along Z to
    % prevent accumulation of noise.
    relPose.Translation(3) = 0;

    % Update absolute pose
    height = absPose.Translation(3);
    absPose = rigid3d( relPose.T * absPose.T );
    absPose.Translation(3) = height;

    % Add new view and connection to previous view
    vSet = addView(vSet, viewId, absPose, 'PointCloud', ptCloud);
    vSet = addConnection(vSet, prevViewId, viewId, relPose);

    % Accumulated point cloud map
    ptCloudAccum = pccat([ptCloudAccum, pctransform(ptCloud, absPose)]);

    % Compute ground truth and estimate position
    localizationEstimatePos = absPose.Translation;
    localizationTruthPos    = lidarLocation(viewId, :);

    % Update accumulated point cloud map
    view(player, ptCloudAccum);

    % Set viewing angle to top view
    view(player.Axes, 2);

    % Convert current absolute pose of sensor to vehicle frame
    absVehiclePose = rigid3d( lidarToVehicleTform.T * absPose.T );

    % Draw vehicle at current absolute pose
    helperDrawVehicle(player.Axes, absVehiclePose, vehicleDims, 'Color', vehicleColor);

    % Draw localization estimate and ground truth points
    helperDrawLocalization(player.Axes, ...
        localizationEstimatePos, estimatePathHandle, vehicleColor, ...
        localizationTruthPos, truthPathHandle, [0 1 0]);

    prevPtCloud = ptCloud;
    prevViewId  = viewId;
end

Zoom in to the tail of the trajectory to examine the localization estimate compared to the ground truth.

xlim(player.Axes, [0 15]);
ylim(player.Axes, [-15 0]);
zlim(player.Axes, [0 15]);

snapnow;

% Close player
hide(player);

A useful outcome of a localization algorithm based on point cloud registration is a map of the traversed environment. You can obtain this map by combining all the point clouds to a common reference frame. The pccat (Computer Vision Toolbox) function is used in each iteration of the loop above, along with pctransform (Computer Vision Toolbox), to incrementally combine the registered point clouds. Alternatively, you can use the pcalign (Computer Vision Toolbox) function to align all point clouds to the common reference frame in one shot at the end.

Superimpose the point cloud map on the top-view image of the scene to visually examine how closely it resembles features in the scene.

hMapOnScene = helperSuperimposeMapOnSceneImage(sceneName, ptCloudAccum);

snapnow;

% Close the figure
close(hMapOnScene);

The localization algorithm described above is encapsulated in the helperLidarRegistrationLocalizer helper class. This class can be used as a framework to develop a localization pipeline using point cloud registration.

  • Use the 'ProcessFcnHandle' and 'ProcessFcnArguments' name-value pair arguments to configure how point clouds are processed prior to registration.

  • Use the 'RegisterFcnHandle' and 'RegisterFcnArguments' name-value pair arguments to configure how point clouds are registered.

Evaluate Localization Accuracy

To quantify the efficacy of localization, measure the deviation in translation and rotation estimates compared to ground truth. Since the vehicle is moving on flat ground, this example is concerned only with motion in the X-Y plane.

hFigMetrics = helperDisplayMetrics(vSet, lidarLocation, lidarOrientation, simTimes);

Simulate in the Loop

Although metrics like deviation in translation and rotation estimates are necessary, the performance of a localization system can have downstream impacts. For example, changes to the accuracy or performance of a localization system can affect the vehicle controller, necessitating the retuning of controller gains. Therefore, it is crucial to have a closed-loop verification framework that incorporates downstream components. The localizeAndControlUsingLidar model demonstrates this framework by incorporating a localization algorithm, vehicle controller and suitable vehicle model.

The model has these main components:

  • The Localize block is a MATLAB Function block that encapsulates the localization algorithm - implemented using the helperLidarRegistrationLocalizer class. This block takes the lidar point cloud generated by the Simulation 3D Lidar block and the initial known pose as inputs and produces a localization estimate. The estimate is returned as $(x, y, \theta)$, which represents the 2-D pose of the lidar in the map reference frame.

  • The Plan subsystem loads a preplanned trajectory from the workspace using the refPoses, directions, curvatures and velocities workspace variables. The Path Smoother Spline block was used to compute the refPoses, directions and curvatures variables. The Velocity Profiler block computed the velocities variable.

  • The Helper Path Analyzer block uses the reference trajectory and the current pose to feed the appropriate reference signal to the vehicle controller.

  • The Vehicle Controller subsystem controls the steering and velocity of the vehicle by using a lateral and longitudinal controller to produce a steering and acceleration or deceleration command. The Lateral Controller Stanley and Longitudinal Controller Stanley blocks are used to implement this. These commands are fed to a vehicle model to simulate the dynamics of the vehicle in the simulation environment using the Vehicle Body 3DOF block.

close(hFigMetrics);

% Load workspace variables for preplanned trajectory
refPoses   = data.ReferencePathForward.Trajectory.refPoses;
directions = data.ReferencePathForward.Trajectory.directions;
curvatures = data.ReferencePathForward.Trajectory.curvatures;
velocities = data.ReferencePathForward.Trajectory.velocities;
startPose  = refPoses(1, :);

% Open model
modelName = 'localizeAndControlUsingLidar';
open_system(modelName);
snapnow;

% Run simulation
sim(modelName);

close_system(modelName);

With this setup, it is possible to rapidly iterate over different scenarios, sensor configurations, or reference trajectories and refine the localization algorithm before moving to real-world testing.

  • To select a different scenario, use the Simulation 3D Scene Configuration block. Choose from the existing prebuilt scenes or create a custom scene in the Unreal® Editor.

  • To create a different reference trajectory, use the helperSelectSceneWaypoints tool, as shown in the Select Waypoints for Unreal Engine Simulation example.

  • To alter the sensor configuration use the Simulation 3D Lidar block. The Mounting tab provides options for specifying different sensor mounting placements. The Parameters tab provides options for modifying sensor parameters such as detection range, field of view, and resolution.

Supporting Functions

helperGetPointClouds Extract an array of pointCloud objects that contain lidar sensor data.

function ptCloudArr = helperGetPointClouds(simOut)

% Extract signal
ptCloudData = simOut.ptCloudData.signals.values;

% Create a pointCloud array
ptCloudArr = pointCloud(ptCloudData(:,:,:,3)); % Ignore first 2 frames
for n = 4 : size(ptCloudData,4)
    ptCloudArr(end+1) = pointCloud(ptCloudData(:,:,:,n));  %#ok<AGROW>
end
end

helperGetLocalizerGroundTruth Extract ground truth location and orientation.

function [lidarLocation, lidarOrientation, simTimes] = helperGetLocalizerGroundTruth(simOut)

lidarLocation    = squeeze(simOut.lidarLocation.signals.values)';
lidarOrientation = squeeze(simOut.lidarOrientation.signals.values)';
simTimes         = simOut.lidarLocation.time;

% Ignore first 2 frames
lidarLocation(1:2, :)    = [];
lidarOrientation(1:2, :) = [];
simTimes(1:2, :)         = [];
end

helperDrawLocalization Draw localization estimate and ground truth on axes.

function [estHandle,truthHandle] = helperDrawLocalization(axesHandle, ...
    est, estHandle, estColor, truth, truthHandle, truthColor)

% Create scatter objects and adjust legend
if isempty(estHandle) || isempty(truthHandle)
    markerSize = 6;
    hold(axesHandle, 'on');
    estHandle   = scatter3(axesHandle, NaN, NaN, NaN, markerSize, estColor, 'filled');
    truthHandle = scatter3(axesHandle, NaN, NaN, NaN, markerSize, truthColor, 'filled');
    %legend(axesHandle, {'Points', 'Estimate', 'Truth'}, ...
    %    'Color', [1 1 1], 'Location', 'northeast');
    hold(axesHandle, 'off');
end

estHandle.XData(end+1) = est(1);
estHandle.YData(end+1) = est(2);
estHandle.ZData(end+1) = est(3);

truthHandle.XData(end+1) = truth(1);
truthHandle.YData(end+1) = truth(2);
truthHandle.ZData(end+1) = truth(3);
end

helperSuperimposeMapOnSceneImage Superimpose point cloud map on scene image

function hFig = helperSuperimposeMapOnSceneImage(sceneName, ptCloudAccum)

hFig = figure('Name', 'Point Cloud Map');
hIm = helperShowSceneImage(sceneName);

hold(hIm.Parent, 'on')
pcshow(ptCloudAccum);
hold(hIm.Parent, 'off')

xlim(hIm.Parent, [-10 50]);
ylim(hIm.Parent, [-30 20]);
end

helperDisplayMetrics Display metrics to assess quality of localization.

function hFig = helperDisplayMetrics(vSet, lidarLocation, lidarOrientation, simTimes)

absPoses = vSet.Views.AbsolutePose;
translationEstimates = vertcat(absPoses.Translation);
rotationEstimates    = pagetranspose(cat(3, absPoses.Rotation));

viewIds = vSet.Views.ViewId;
viewTimes = simTimes(viewIds);

xEst   = translationEstimates(:, 1);
yEst   = translationEstimates(:, 2);
yawEst = euler(quaternion(rotationEstimates, 'rotmat', 'point'), 'ZYX', 'point');
yawEst = yawEst(:, 1);

xTruth   = lidarLocation(viewIds, 1);
yTruth   = lidarLocation(viewIds, 2);
yawTruth = lidarOrientation(viewIds, 3);

xDeviation   = abs(xEst - xTruth);
yDeviation   = abs(yEst - yTruth);
yawDeviation = abs(helperWrapToPi(yawTruth - yawEst));

hFig = figure('Name', 'Metrics - Absolute Deviation');
subplot(3,1,1)
plot(viewTimes, xDeviation, 'LineWidth', 2);
ylim([0 1])
grid on
title('X')
xlabel('Time (s)')
ylabel('Deviation (m)')

subplot(3,1,2)
plot(viewTimes, yDeviation, 'LineWidth', 2);
ylim([0 1])
grid on
title('Y')
xlabel('Time (s)')
ylabel('Deviation (m)')

subplot(3,1,3)
plot(viewTimes, yawDeviation, 'LineWidth', 2);
ylim([0 pi/20])
grid on
title('Yaw')
xlabel('Time (s)')
ylabel('Deviation (rad)')
end

helperWrapToPi Wrap angles to be in the range $[-\pi, \pi]$.

function angle = helperWrapToPi(angle)

idx = (angle < -pi) | (angle > pi);
angle(idx) = helperWrapTo2Pi(angle(idx) + pi) - pi;
end

helperWrapTo2Pi Wrap angles to be in the range $[-2\pi, 2\pi]$.

function angle = helperWrapTo2Pi(angle)

pos = (angle>0);
angle = mod(angle, 2*pi);
angle(angle==0 & pos) = 2*pi;
end

See Also

Functions

Blocks

Related Topics