sensor-simulation
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<!-- URDF snippet for LiDAR sensor -->
<gazebo reference="lidar_link">
<sensor name="lidar" type="ray">
<pose>0 0 0 0 0 0</pose>
<visualize>true</visualize>
<update_rate>10</update_rate>
<ray>
<scan>
<horizontal>
<samples>360</samples>
<resolution>1</resolution>
<min_angle>-3.14159</min_angle>
<max_angle>3.14159</max_angle>
</horizontal>
</scan>
<range>
<min>0.1</min>
<max>30.0</max>
<resolution>0.01</resolution>
</range>
</ray>
<plugin name="lidar_controller" filename="libgazebo_ros_ray_sensor.so">
<ros>
<namespace>lidar</namespace>
<remapping>~/out:=scan</remapping>
</ros>
<output_type>sensor_msgs/LaserScan</output_type>
</plugin>
</sensor>
</gazebo>
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// Scripts/LiDARSimulator.cs
using UnityEngine;
using System.Collections.Generic;
public class LiDARSimulator : MonoBehaviour
{
[Header("LiDAR Configuration")]
public int beamCount = 360;
public float minAngle = -Mathf.PI;
public float maxAngle = Mathf.PI;
public float maxRange = 30f;
public float updateRate = 10f; // Hz
public LayerMask detectionLayers = -1;
[Header("Noise Parameters")]
public float noiseStdDev = 0.01f;
public float biasError = 0f;
[Header("Visualization")]
public bool visualizeBeams = true;
public LineRenderer lineRenderer;
private float[] ranges;
private float updateInterval;
private float lastUpdate;
void Start()
{
ranges = new float[beamCount];
updateInterval = 1f / updateRate;
lastUpdate = -updateInterval; // Allow immediate first update
if (lineRenderer == null)
{
lineRenderer = GetComponent<LineRenderer>();
if (lineRenderer == null)
{
lineRenderer = gameObject.AddComponent<LineRenderer>();
lineRenderer.material = new Material(Shader.Find("Sprites/Default"));
lineRenderer.startWidth = 0.01f;
lineRenderer.endWidth = 0.01f;
lineRenderer.positionCount = beamCount;
}
}
}
void Update()
{
if (Time.time - lastUpdate >= updateInterval)
{
SimulateLiDAR();
lastUpdate = Time.time;
}
}
void SimulateLiDAR()
{
for (int i = 0; i < beamCount; i++)
{
float angle = Mathf.Lerp(minAngle, maxAngle, (float)i / (beamCount - 1));
Vector3 direction = new Vector3(Mathf.Cos(angle), 0, Mathf.Sin(angle));
// Perform raycast
RaycastHit hit;
if (Physics.Raycast(transform.position, transform.TransformDirection(direction), out hit, maxRange, detectionLayers))
{
float distance = hit.distance;
// Add noise
float noisyDistance = AddNoise(distance);
ranges[i] = noisyDistance;
}
else
{
ranges[i] = float.MaxValue; // No hit
}
}
if (visualizeBeams)
{
UpdateVisualization();
}
// Publish simulated data (in a real implementation, this would publish to ROS)
PublishLiDARData();
}
float AddNoise(float trueValue)
{
// Add Gaussian noise
float noise = RandomGaussian() * noiseStdDev;
return trueValue + noise + biasError;
}
float RandomGaussian()
{
// Box-Muller transform for Gaussian random numbers
float u1 = Random.value;
float u2 = Random.value;
return Mathf.Sqrt(-2f * Mathf.Log(u1)) * Mathf.Cos(2f * Mathf.PI * u2);
}
void UpdateVisualization()
{
Vector3[] positions = new Vector3[beamCount];
for (int i = 0; i < beamCount; i++)
{
float angle = Mathf.Lerp(minAngle, maxAngle, (float)i / (beamCount - 1));
Vector3 direction = new Vector3(Mathf.Cos(angle), 0, Mathf.Sin(angle));
if (ranges[i] < maxRange)
{
positions[i] = transform.position + transform.TransformDirection(direction) * ranges[i];
}
else
{
positions[i] = transform.position + transform.TransformDirection(direction) * maxRange * 0.9f;
}
}
lineRenderer.SetPositions(positions);
}
void PublishLiDARData()
{
// In a real implementation, this would publish to ROS/ROS 2
// For now, we'll just log the data
Debug.Log($"LiDAR: {beamCount} beams, first range: {ranges[0]:F3}m");
}
public float[] GetRanges()
{
return ranges;
}
public float GetRange(int beamIndex)
{
if (beamIndex >= 0 && beamIndex < ranges.Length)
{
return ranges[beamIndex];
}
return float.MaxValue;
}
}
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// Scripts/DepthCameraSimulator.cs
using UnityEngine;
using System.Collections;
public class DepthCameraSimulator : MonoBehaviour
{
[Header("Camera Configuration")]
public int width = 640;
public int height = 480;
public float fieldOfView = 60f;
public float nearClip = 0.1f;
public float farClip = 10f;
[Header("Noise Parameters")]
public float noiseStdDev = 0.02f;
public float biasError = 0f;
public float depthScale = 1f;
[Header("Output Settings")]
public bool outputPointCloud = true;
public bool outputDepthImage = true;
private Camera depthCamera;
private RenderTexture depthTexture;
private float[,] depthData;
private GameObject pointCloudObject;
void Start()
{
SetupCamera();
CreateDepthTexture();
depthData = new float[width, height];
}
void SetupCamera()
{
depthCamera = GetComponent<Camera>();
if (depthCamera == null)
{
depthCamera = gameObject.AddComponent<Camera>();
}
depthCamera.fieldOfView = fieldOfView;
depthCamera.nearClipPlane = nearClip;
depthCamera.farClipPlane = farClip;
depthCamera.depth = -1; // Render after other cameras
depthCamera.clearFlags = CameraClearFlags.SolidColor;
depthCamera.backgroundColor = Color.white;
depthCamera.enabled = false; // We'll render manually
}
void CreateDepthTexture()
{
depthTexture = new RenderTexture(width, height, 24, RenderTextureFormat.Depth);
depthTexture.Create();
depthCamera.targetTexture = depthTexture;
}
void Update()
{
SimulateDepthCamera();
}
void SimulateDepthCamera()
{
// Render the scene from this camera's perspective
depthCamera.Render();
// Read depth data from render texture
ReadDepthData();
// Add noise to simulate real sensor characteristics
AddNoiseToDepthData();
if (outputPointCloud)
{
GeneratePointCloud();
}
if (outputDepthImage)
{
OutputDepthImage();
}
}
void ReadDepthData()
{
// Create a temporary render texture to read the depth data
RenderTexture currentRT = RenderTexture.active;
RenderTexture.active = depthTexture;
Texture2D depthTex = new Texture2D(width, height, TextureFormat.RFloat, false);
depthTex.ReadPixels(new Rect(0, 0, width, height), 0, 0);
depthTex.Apply();
// Convert texture data to depth values
Color[] pixels = depthTex.GetPixels();
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
int index = y * width + x;
float rawDepth = pixels[index].r;
// Convert raw depth to actual distance
// This is a simplified conversion - real implementation would be more complex
float actualDepth = ConvertRawDepthToDistance(rawDepth);
depthData[x, y] = actualDepth;
}
}
// Clean up
RenderTexture.active = currentRT;
DestroyImmediate(depthTex);
}
float ConvertRawDepthToDistance(float rawDepth)
{
// Convert raw depth buffer value to actual distance
// This is a simplified conversion
float zNear = depthCamera.nearClipPlane;
float zFar = depthCamera.farClipPlane;
// Convert from [0,1] to actual distance
float linearDepth = (2.0f * zNear * zFar) / (zFar + zNear - rawDepth * (zFar - zNear));
return linearDepth;
}
void AddNoiseToDepthData()
{
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
if (depthData[x, y] < farClip) // Only add noise to valid measurements
{
float noise = RandomGaussian() * noiseStdDev;
depthData[x, y] += noise + biasError;
// Ensure depth is within valid range
depthData[x, y] = Mathf.Clamp(depthData[x, y], nearClip, farClip);
}
}
}
}
float RandomGaussian()
{
// Box-Muller transform for Gaussian random numbers
float u1 = Random.value;
float u2 = Random.value;
return Mathf.Sqrt(-2f * Mathf.Log(u1)) * Mathf.Cos(2f * Mathf.PI * u2);
}
void GeneratePointCloud()
{
// Generate point cloud from depth data
Vector3[] points = new Vector3[width * height];
int pointCount = 0;
float fovX = fieldOfView * Mathf.Deg2Rad;
float fovY = (fieldOfView * height / width) * Mathf.Deg2Rad;
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
float depth = depthData[x, y];
if (depth > 0 && depth < farClip) // Valid depth measurement
{
// Convert pixel coordinates to 3D coordinates
float u = (float)x / width - 0.5f; // -0.5 to 0.5
float v = (float)y / height - 0.5f; // -0.5 to 0.5
float x3d = u * depth * Mathf.Tan(fovX / 2) * 2;
float y3d = -v * depth * Mathf.Tan(fovY / 2) * 2; // Negative because screen coordinates are inverted
float z3d = depth;
Vector3 point = transform.TransformPoint(new Vector3(x3d, y3d, z3d));
points[pointCount] = point;
pointCount++;
}
}
}
// Resize array to actual point count
System.Array.Resize(ref points, pointCount);
// In a real implementation, you would publish this point cloud to ROS
Debug.Log($"Generated point cloud with {pointCount} points");
}
void OutputDepthImage()
{
// Create a texture to visualize the depth data
Texture2D depthVisualization = new Texture2D(width, height, TextureFormat.RGB24, false);
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
float depth = depthData[x, y];
// Normalize depth for visualization (0 = near, 1 = far)
float normalizedDepth = Mathf.InverseLerp(nearClip, farClip, depth);
// Map to grayscale color
Color color = Color.Lerp(Color.white, Color.black, normalizedDepth);
depthVisualization.SetPixel(x, y, color);
}
}
depthVisualization.Apply();
// In a real implementation, you would publish this image to ROS
}
public float[,] GetDepthData()
{
return depthData;
}
public float GetDepthAt(int x, int y)
{
if (x >= 0 && x < width && y >= 0 && y < height)
{
return depthData[x, y];
}
return float.MaxValue;
}
}
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// Scripts/IMUSimulator.cs
using UnityEngine;
[System.Serializable]
public struct IMUReading
{
public Vector3 linearAcceleration; // m/s^2
public Vector3 angularVelocity; // rad/s
public Vector3 magneticField; // Tesla
public Vector3 orientation; // Euler angles in radians
public double timestamp; // Simulation time
}
public class IMUSimulator : MonoBehaviour
{
[Header("Accelerometer Parameters")]
public float accelerometerNoiseDensity = 8.75e-4f; // m/s^2 / sqrt(Hz)
public float accelerometerRandomWalk = 2.92e-5f; // m/s^3 / sqrt(Hz)
public Vector3 accelerometerBias = Vector3.zero;
public float accelerometerScaleFactorError = 0.001f; // 0.1%
[Header("Gyroscope Parameters")]
public float gyroscopeNoiseDensity = 1.64e-4f; // rad/s / sqrt(Hz)
public float gyroscopeRandomWalk = 5.42e-6f; // rad/s^2 / sqrt(Hz)
public Vector3 gyroscopeBias = Vector3.zero;
public float gyroscopeScaleFactorError = 0.001f; // 0.1%
[Header("Magnetometer Parameters")]
public float magnetometerNoise = 0.05e-6f; // Tesla
public Vector3 magnetometerBias = Vector3.zero;
[Header("Simulation Parameters")]
public float updateRate = 100f; // Hz
public bool enableDrift = true;
private IMUReading currentReading;
private float updateInterval;
private float lastUpdate;
// Bias drift accumulators
private Vector3 accelerometerBiasDrift = Vector3.zero;
private Vector3 gyroscopeBiasDrift = Vector3.zero;
void Start()
{
updateInterval = 1f / updateRate;
lastUpdate = -updateInterval; // Allow immediate first update
// Initialize with current transform values
UpdateIMUReading();
}
void Update()
{
if (Time.time - lastUpdate >= updateInterval)
{
UpdateIMUReading();
lastUpdate = Time.time;
}
}
void UpdateIMUReading()
{
// Get true values from Unity's physics
Vector3 trueLinearAcceleration = GetTrueLinearAcceleration();
Vector3 trueAngularVelocity = GetTrueAngularVelocity();
Vector3 trueMagneticField = GetTrueMagneticField();
Vector3 trueOrientation = transform.eulerAngles * Mathf.Deg2Rad;
// Add noise and errors
currentReading.linearAcceleration = AddAccelerometerNoise(trueLinearAcceleration);
currentReading.angularVelocity = AddGyroscopeNoise(trueAngularVelocity);
currentReading.magneticField = AddMagnetometerNoise(trueMagneticField);
currentReading.orientation = trueOrientation; // Add noise to orientation if needed
currentReading.timestamp = Time.time;
// Publish simulated data
PublishIMUData();
}
Vector3 GetTrueLinearAcceleration()
{
// Calculate true linear acceleration from Unity physics
// This is a simplified approach - in practice, you'd need to account for gravity
Rigidbody rb = GetComponent<Rigidbody>();
if (rb != null)
{
// Remove gravity from the acceleration
return rb.velocity - Physics.gravity * Time.deltaTime;
}
else
{
// If no rigidbody, use transform changes
return (transform.position - transform.position) / (Time.deltaTime * Time.deltaTime);
}
}
Vector3 GetTrueAngularVelocity()
{
// Calculate true angular velocity from Unity physics
Rigidbody rb = GetComponent<Rigidbody>();
if (rb != null)
{
return rb.angularVelocity;
}
else
{
// If no rigidbody, approximate from rotation change
return Vector3.zero; // Simplified - would need to track rotation over time
}
}
Vector3 GetTrueMagneticField()
{
// Return a typical Earth magnetic field vector
// This should be adjusted based on geographic location
return new Vector3(0.22f, 0.0f, 0.45f) * 1e-6f; // Tesla
}
Vector3 AddAccelerometerNoise(Vector3 trueAcceleration)
{
Vector3 noise = new Vector3(
RandomGaussian() * accelerometerNoiseDensity,
RandomGaussian() * accelerometerNoiseDensity,
RandomGaussian() * accelerometerNoiseDensity
);
// Add bias (with drift if enabled)
Vector3 bias = accelerometerBias + accelerometerBiasDrift;
// Add scale factor error
Vector3 scaledAcceleration = new Vector3(
trueAcceleration.x * (1 + accelerometerScaleFactorError),
trueAcceleration.y * (1 + accelerometerScaleFactorError),
trueAcceleration.z * (1 + accelerometerScaleFactorError)
);
// Add random walk to bias
if (enableDrift)
{
accelerometerBiasDrift += new Vector3(
RandomGaussian() * accelerometerRandomWalk * Mathf.Sqrt(updateInterval),
RandomGaussian() * accelerometerRandomWalk * Mathf.Sqrt(updateInterval),
RandomGaussian() * accelerometerRandomWalk * Mathf.Sqrt(updateInterval)
);
}
return scaledAcceleration + noise + bias;
}
Vector3 AddGyroscopeNoise(Vector3 trueAngularVelocity)
{
Vector3 noise = new Vector3(
RandomGaussian() * gyroscopeNoiseDensity,
RandomGaussian() * gyroscopeNoiseDensity,
RandomGaussian() * gyroscopeNoiseDensity
);
// Add bias (with drift if enabled)
Vector3 bias = gyroscopeBias + gyroscopeBiasDrift;
// Add scale factor error
Vector3 scaledAngularVelocity = new Vector3(
trueAngularVelocity.x * (1 + gyroscopeScaleFactorError),
trueAngularVelocity.y * (1 + gyroscopeScaleFactorError),
trueAngularVelocity.z * (1 + gyroscopeScaleFactorError)
);
// Add random walk to bias
if (enableDrift)
{
gyroscopeBiasDrift += new Vector3(
RandomGaussian() * gyroscopeRandomWalk * Mathf.Sqrt(updateInterval),
RandomGaussian() * gyroscopeRandomWalk * Mathf.Sqrt(updateInterval),
RandomGaussian() * gyroscopeRandomWalk * Mathf.Sqrt(updateInterval)
);
}
return scaledAngularVelocity + noise + bias;
}
Vector3 AddMagnetometerNoise(Vector3 trueMagneticField)
{
Vector3 noise = new Vector3(
RandomGaussian() * magnetometerNoise,
RandomGaussian() * magnetometerNoise,
RandomGaussian() * magnetometerNoise
);
// Add bias
Vector3 bias = magnetometerBias;
return trueMagneticField + noise + bias;
}
float RandomGaussian()
{
// Box-Muller transform for Gaussian random numbers
float u1 = Random.value;
float u2 = Random.value;
return Mathf.Sqrt(-2f * Mathf.Log(u1)) * Mathf.Cos(2f * Mathf.PI * u2);
}
void PublishIMUData()
{
// In a real implementation, this would publish to ROS/ROS 2
Debug.Log($"IMU: Acc=({currentReading.linearAcceleration.x:F3}, {currentReading.linearAcceleration.y:F3}, {currentReading.linearAcceleration.z:F3})");
}
public IMUReading GetIMUReading()
{
return currentReading;
}
public void ResetBiasDrift()
{
accelerometerBiasDrift = Vector3.zero;
gyroscopeBiasDrift = Vector3.zero;
}
}
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// Scripts/SensorFusion.cs
using UnityEngine;
using System.Collections.Generic;
public class SensorFusion : MonoBehaviour
{
[Header("Sensor References")]
public LiDARSimulator lidar;
public DepthCameraSimulator depthCamera;
public IMUSimulator imu;
[Header("Fusion Parameters")]
public float lidarWeight = 0.4f;
public float depthWeight = 0.3f;
public float imuWeight = 0.3f;
private Queue<IMUReading> imuBuffer;
private float bufferDuration = 1.0f; // seconds
void Start()
{
imuBuffer = new Queue<IMUReading>();
}
void Update()
{
// Integrate sensor data
ProcessSensorData();
}
void ProcessSensorData()
{
// Get current readings from all sensors
float[] lidarData = lidar != null ? lidar.GetRanges() : new float[0];
float[,] depthData = depthCamera != null ? depthCamera.GetDepthData() : new float[0, 0];
IMUReading imuData = imu != null ? imu.GetIMUReading() : new IMUReading();
// Store IMU data in buffer for temporal fusion
if (imu != null)
{
imuBuffer.Enqueue(imuData);
// Remove old data from buffer
while (imuBuffer.Count > 0 &&
Time.time - imuBuffer.Peek().timestamp > bufferDuration)
{
imuBuffer.Dequeue();
}
}
// Perform sensor fusion (simplified example)
PerformFusion(lidarData, depthData, imuData);
}
void PerformFusion(float[] lidarData, float[,] depthData, IMUReading imuData)
{
// Example fusion algorithm: combine position estimates
Vector3 positionEstimate = Vector3.zero;
// Calculate position from IMU integration
if (imuBuffer.Count > 1)
{
IMUReading first = imuBuffer.Peek();
IMUReading last = imuBuffer.ToArray()[imuBuffer.Count - 1];
float deltaTime = (float)(last.timestamp - first.timestamp);
if (deltaTime > 0)
{
Vector3 velocity = (last.linearAcceleration + first.linearAcceleration) * 0.5f * deltaTime;
positionEstimate += velocity * imuWeight;
}
}
// In a real implementation, you would combine this with LiDAR and depth data
// for more accurate position estimation
Debug.Log($"Fused position estimate: {positionEstimate}");
}
public Vector3 GetFusedPositionEstimate()
{
// Return the fused position estimate
return transform.position; // Simplified
}
}
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// Scripts/ROSPublisher.cs
using UnityEngine;
using Unity.Robotics.ROSTCPConnector;
using RosMessageTypes.Sensor;
using RosMessageTypes.Geometry;
public class ROSPublisher : MonoBehaviour
{
private ROSConnection ros;
[Header("Topic Names")]
public string laserScanTopic = "scan";
public string pointCloudTopic = "point_cloud";
public string imuTopic = "imu/data";
[Header("Sensors")]
public LiDARSimulator lidar;
public DepthCameraSimulator depthCamera;
public IMUSimulator imu;
void Start()
{
ros = ROSConnection.instance;
}
void Update()
{
PublishSensorData();
}
void PublishSensorData()
{
if (lidar != null)
{
PublishLaserScan();
}
if (depthCamera != null)
{
PublishPointCloud();
}
if (imu != null)
{
PublishIMU();
}
}
void PublishLaserScan()
{
var scanMsg = new sensor_msgs.LaserScanMsg();
scanMsg.header = new std_msgs.HeaderMsg();
scanMsg.header.stamp = new builtin_interfaces.TimeMsg();
scanMsg.header.frame_id = "lidar_frame";
float[] ranges = lidar.GetRanges();
scanMsg.ranges = new float[ranges.Length];
for (int i = 0; i < ranges.Length; i++)
{
scanMsg.ranges[i] = ranges[i];
}
scanMsg.angle_min = lidar.minAngle;
scanMsg.angle_max = lidar.maxAngle;
scanMsg.angle_increment = (lidar.maxAngle - lidar.minAngle) / (ranges.Length - 1);
scanMsg.range_min = 0.1f;
scanMsg.range_max = lidar.maxRange;
ros.Publish(laserScanTopic, scanMsg);
}
void PublishPointCloud()
{
// Publish point cloud from depth camera
// Implementation would convert depth data to PointCloud2 message
}
void PublishIMU()
{
IMUReading reading = imu.GetIMUReading();
var imuMsg = new sensor_msgs.ImuMsg();
imuMsg.header = new std_msgs.HeaderMsg();
imuMsg.header.stamp = new builtin_interfaces.TimeMsg();
imuMsg.header.frame_id = "imu_frame";
// Convert to ROS message format
imuMsg.linear_acceleration.x = reading.linearAcceleration.x;
imuMsg.linear_acceleration.y = reading.linearAcceleration.y;
imuMsg.linear_acceleration.z = reading.linearAcceleration.z;
imuMsg.angular_velocity.x = reading.angularVelocity.x;
imuMsg.angular_velocity.y = reading.angularVelocity.y;
imuMsg.angular_velocity.z = reading.angularVelocity.z;
// Convert Euler angles to quaternion
imuMsg.orientation = Unity.Robotics.ROSTCPConnector.MessageExtensions.To<geometry_msgs.QuaternionMsg>(
Quaternion.Euler(reading.orientation * Mathf.Rad2Deg)
);
ros.Publish(imuTopic, imuMsg);
}
}
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