Adds lidar pattern for raycaster sensor (#616)

# Description

Add a lidar pattern for the `RayCaster` sensor. The pattern is
determined by number of channels, horizontal and vertical field of view
and corresponding horizontal resolution. An example config for a
Velodyne VLP 16 is provided.

## Type of change

- New feature (non-breaking change which adds functionality)

## Screenshots

![Screenshot from 2024-07-02
10-13-45](https://github.com/isaac-sim/IsaacLab/assets/57946385/31c87608-9d4d-4a11-9ad5-1ced954aa750)

## Checklist

- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [ ] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [x] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
- [x] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there

docs/source/api/lab/omni.isaac.lab.sensors.patterns.rst

@@ -49,3 +49,13 @@ RS-Bpearl Pattern
     :members:
     :inherited-members:
     :exclude-members: __init__, func
+
+LiDAR Pattern
+-------------
+
+.. autofunction:: omni.isaac.lab.sensors.patterns.lidar_pattern
+
+.. autoclass:: LidarPatternCfg
+    :members:
+    :inherited-members:
+    :exclude-members: __init__, func

source/extensions/omni.isaac.lab/config/extension.toml

 [package]
 
 # Note: Semantic Versioning is used: https://semver.org/
-version = "0.19.0"
+version = "0.19.1"
 
 # Description
 title = "Isaac Lab framework for Robot Learning"

source/extensions/omni.isaac.lab/docs/CHANGELOG.rst

 Changelog
 ---------
 
+0.19.1 (2024-07-05)
+~~~~~~~~~~~~~~~~~~~
+
+Added
+^^^^^
+
+* Added a lidar pattern function :func:`~omni.isaac.lab.sensors.ray_caster.patterns.patterns.lidar_pattern` with
+  corresponding config :class:`~omni.isaac.lab.sensors.ray_caster.patterns_cfg.LidarPatternCfg`.
+
+
 0.19.0 (2024-07-04)
 ~~~~~~~~~~~~~~~~~~~
 

source/extensions/omni.isaac.lab/omni/isaac/lab/sensors/ray_caster/patterns/__init__.py

@@ -5,5 +5,5 @@
 
 """Sub-module for ray-casting patterns used by the ray-caster."""
 
-from .patterns import bpearl_pattern, grid_pattern, pinhole_camera_pattern
-from .patterns_cfg import BpearlPatternCfg, GridPatternCfg, PatternBaseCfg, PinholeCameraPatternCfg
+from .patterns import bpearl_pattern, grid_pattern, lidar_pattern, pinhole_camera_pattern
+from .patterns_cfg import BpearlPatternCfg, GridPatternCfg, LidarPatternCfg, PatternBaseCfg, PinholeCameraPatternCfg

source/extensions/omni.isaac.lab/omni/isaac/lab/sensors/ray_caster/patterns/patterns.py

@@ -5,6 +5,7 @@
 
 from __future__ import annotations
 
+import math
 import torch
 from typing import TYPE_CHECKING
 
@@ -128,3 +129,49 @@ def bpearl_pattern(cfg: patterns_cfg.BpearlPatternCfg, device: str) -> tuple[tor
     ray_directions = -torch.stack([x, y, z], dim=1)
     ray_starts = torch.zeros_like(ray_directions)
     return ray_starts, ray_directions
+
+
+def lidar_pattern(cfg: patterns_cfg.LidarPatternCfg, device: str) -> tuple[torch.Tensor, torch.Tensor]:
+    """Lidar sensor pattern for ray casting.
+
+    Args:
+        cfg: The configuration instance for the pattern.
+        device: The device to create the pattern on.
+
+    Returns:
+        The starting positions and directions of the rays.
+    """
+    # Vertical angles
+    vertical_angles = torch.linspace(cfg.vertical_fov_range[0], cfg.vertical_fov_range[1], cfg.channels)
+
+    # If the horizontal field of view is 360 degrees, exclude the last point to avoid overlap
+    if abs(abs(cfg.horizontal_fov_range[0] - cfg.horizontal_fov_range[1]) - 360.0) < 1e-6:
+        up_to = -1
+    else:
+        up_to = None
+
+    # Horizontal angles
+    num_horizontal_angles = math.ceil((cfg.horizontal_fov_range[1] - cfg.horizontal_fov_range[0]) / cfg.horizontal_res)
+    horizontal_angles = torch.linspace(cfg.horizontal_fov_range[0], cfg.horizontal_fov_range[1], num_horizontal_angles)[
+        :up_to
+    ]
+
+    # Convert degrees to radians
+    vertical_angles_rad = torch.deg2rad(vertical_angles)
+    horizontal_angles_rad = torch.deg2rad(horizontal_angles)
+
+    # Meshgrid to create a 2D array of angles
+    v_angles, h_angles = torch.meshgrid(vertical_angles_rad, horizontal_angles_rad, indexing="ij")
+
+    # Spherical to Cartesian conversion (assuming Z is up)
+    x = torch.cos(v_angles) * torch.cos(h_angles)
+    y = torch.cos(v_angles) * torch.sin(h_angles)
+    z = torch.sin(v_angles)
+
+    # Ray directions
+    ray_directions = torch.stack([x, y, z], dim=-1).reshape(-1, 3).to(device)
+
+    # Ray starts: Assuming all rays originate from (0,0,0)
+    ray_starts = torch.zeros_like(ray_directions).to(device)
+
+    return ray_starts, ray_directions

source/extensions/omni.isaac.lab/omni/isaac/lab/sensors/ray_caster/patterns/patterns_cfg.py

@@ -122,3 +122,22 @@ class BpearlPatternCfg(PatternBaseCfg):
         We manually set the vertical ray angles to match the Bpearl sensor. The ray-angles
         are not evenly spaced.
     """
+
+
+@configclass
+class LidarPatternCfg(PatternBaseCfg):
+    """Configuration for the LiDAR pattern for ray-casting."""
+
+    func: Callable = patterns.lidar_pattern
+
+    channels: int = MISSING
+    """Number of Channels (Beams). Determines the vertical resolution of the LiDAR sensor."""
+
+    vertical_fov_range: tuple[float, float] = MISSING
+    """Vertical field of view range in degrees."""
+
+    horizontal_fov_range: tuple[float, float] = MISSING
+    """Horizontal field of view range in degrees."""
+
+    horizontal_res: float = MISSING
+    """Horizontal resolution (in degrees)."""

source/extensions/omni.isaac.lab_assets/omni/isaac/lab_assets/anymal.py

@@ -22,8 +22,11 @@ Reference:
 import omni.isaac.lab.sim as sim_utils
 from omni.isaac.lab.actuators import ActuatorNetLSTMCfg, DCMotorCfg
 from omni.isaac.lab.assets.articulation import ArticulationCfg
+from omni.isaac.lab.sensors import RayCasterCfg
 from omni.isaac.lab.utils.assets import ISAACLAB_NUCLEUS_DIR
 
+from .velodyne import VELODYNE_VLP_16_RAYCASTER_CFG
+
 ##
 # Configuration - Actuators.
 ##
@@ -160,3 +163,13 @@ Note:
     Since we don't have a publicly available actuator network for ANYmal-D, we use the same network as ANYmal-C.
     This may impact the sim-to-real transfer performance.
 """
+
+
+##
+# Configuration - Sensors.
+##
+
+ANYMAL_LIDAR_CFG = VELODYNE_VLP_16_RAYCASTER_CFG.replace(
+    offset=RayCasterCfg.OffsetCfg(pos=(-0.310, 0.000, 0.159), rot=(0.0, 0.0, 0.0, 1.0))
+)
+"""Configuration for the Velodyne VLP-16 sensor mounted on the ANYmal robot's base."""

source/extensions/omni.isaac.lab_assets/omni/isaac/lab_assets/velodyne.py

+# Copyright (c) 2022-2024, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""Configuration for Velodyne LiDAR sensors."""
+
+
+from omni.isaac.lab.sensors import RayCasterCfg, patterns
+
+##
+# Configuration
+##
+
+VELODYNE_VLP_16_RAYCASTER_CFG = RayCasterCfg(
+    attach_yaw_only=False,
+    pattern_cfg=patterns.LidarPatternCfg(
+        channels=16, vertical_fov_range=(-15.0, 15.0), horizontal_fov_range=(-180.0, 180.0), horizontal_res=0.2
+    ),
+    debug_vis=True,
+    max_distance=100,
+)
+"""Configuration for Velodyne Puck LiDAR (VLP-16) as a :class:`RayCasterCfg`.
+
+Reference: https://velodynelidar.com/wp-content/uploads/2019/12/63-9229_Rev-K_Puck-_Datasheet_Web.pdf
+"""