📡 Raycaster Patterns

Genesis provides multiple ray patterns for LiDAR and depth sensor simulation.

Pattern Types

Pattern	Use Case
SphericalPattern	3D LiDAR (Velodyne, Ouster)
DepthCameraPattern	Depth cameras (RealSense, Kinect)
GridPattern	Planar sensing, height maps

SphericalPattern (LiDAR)

import genesis as gs

# 360° horizontal, 60° vertical FOV
pattern = gs.sensors.SphericalPattern(
    fov=(360.0, 60.0),
    n_points=(128, 32),
)

lidar = scene.add_sensor(
    gs.sensors.Lidar(
        pattern=pattern,
        entity_idx=robot.idx,
        pos_offset=(0.0, 0.0, 0.15),
        max_range=100.0,
        min_range=0.1,
        draw_debug=True,
    )
)

SphericalPattern Parameters

gs.sensors.SphericalPattern(
    fov=(360.0, 60.0),              # (horizontal, vertical) degrees
    n_points=(128, 64),             # (horizontal, vertical) rays
    angular_resolution=(0.25, 0.5), # Alternative: degrees per ray
    angles=(h_angles, v_angles),    # Custom angle arrays
)

Real LiDAR Configurations

# Velodyne VLP-16
velodyne = gs.sensors.SphericalPattern(fov=(360.0, 30.0), n_points=(1800, 16))

# Front-facing 120° FOV
front_lidar = gs.sensors.SphericalPattern(fov=((-60, 60), 30.0), n_points=(128, 32))

DepthCameraPattern

pattern = gs.sensors.DepthCameraPattern(
    res=(640, 480),
    fov_horizontal=87.0,
)

depth_cam = scene.add_sensor(
    gs.sensors.DepthCamera(
        pattern=pattern,
        entity_idx=robot.idx,
        pos_offset=(0.0, 0.0, 0.05),
        max_range=5.0,
    )
)

DepthCameraPattern Parameters

gs.sensors.DepthCameraPattern(
    res=(640, 480),           # Resolution (width, height)
    fov_horizontal=90.0,      # Horizontal FOV degrees
    fov_vertical=None,        # Auto-computed from aspect ratio
    fx=None, fy=None,         # Focal lengths (override FOV)
    cx=None, cy=None,         # Principal point
)

GridPattern

Planar grid of parallel rays:

pattern = gs.sensors.GridPattern(
    resolution=0.1,            # 10cm spacing
    size=(2.0, 2.0),           # 2m x 2m grid
    direction=(0.0, 0.0, -1.0), # Pointing down
)

Reading Sensor Data

scene.build()
scene.step()

# LiDAR data
data = lidar.read()
points = data.points         # Shape: (n_h, n_v, 3)
distances = data.distances   # Shape: (n_h, n_v)

# Depth camera image
depth_image = depth_cam.read_image()  # Shape: (H, W)

Common Options

gs.sensors.Lidar(
    pattern=pattern,
    entity_idx=robot.idx,
    pos_offset=(0.0, 0.0, 0.15),
    euler_offset=(0.0, 0.0, 0.0),
    max_range=100.0,
    min_range=0.1,
    return_world_frame=True,
    draw_debug=True,
)

Multi-Environment

scene.build(n_envs=4)
data = lidar.read()
print(data.points.shape)  # (4, n_h, n_v, 3) for batched envs