Adds documentation and demo script for IMU sensor (#1694)

# Description

Adds a new documentation page and demo script for explaining the IMU
sensor.
Also replace previous .png images with .jpg files for better storage
efficiency.

## Type of change

- This change requires a documentation update


## Checklist

- [X] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [X] I have made corresponding changes to the documentation
- [X] My changes generate no new warnings
- [X] 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

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---------
Signed-off-by: Michael Gussert <michael@gussert.com>
Co-authored-by: James Tigue <166445701+jtigue-bdai@users.noreply.github.com>

docs/source/overview/sensors/camera.rst

@@ -71,7 +71,7 @@ Both :class:`~sensors.TiledCamera` and :class:`~sensors.Camera` classes provide
 RGB and RGBA
 ~~~~~~~~~~~~
 
-.. figure:: ../../_static/overview/overview_sensors_rgb.png
+.. figure:: ../../_static/overview/overview_sensors_rgb.jpg
     :align: center
     :figwidth: 100%
     :alt: A scene captured in RGB
@@ -85,7 +85,7 @@ To convert the ``torch.uint8`` data to ``torch.float32``, divide the buffer by 2
 Depth and Distances
 ~~~~~~~~~~~~~~~~~~~
 
-.. figure:: ../../_static/overview/overview_sensors_depth.png
+.. figure:: ../../_static/overview/overview_sensors_depth.jpg
     :align: center
     :figwidth: 100%
     :alt: A scene captured in RGB
@@ -99,7 +99,7 @@ Depth and Distances
 Normals
 ~~~~~~~
 
-.. figure:: ../../_static/overview/overview_sensors_normals.png
+.. figure:: ../../_static/overview/overview_sensors_normals.jpg
     :align: center
     :figwidth: 100%
     :alt: A scene captured in RGB
@@ -114,7 +114,7 @@ Motion Vectors
 Semantic Segmentation
 ~~~~~~~~~~~~~~~~~~~~~
 
-.. figure:: ../../_static/overview/overview_sensors_semantic.png
+.. figure:: ../../_static/overview/overview_sensors_semantic.jpg
     :align: center
     :figwidth: 100%
     :alt: A scene captured in RGB
@@ -128,7 +128,7 @@ Semantic Segmentation
 Instance ID Segmentation
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. figure:: ../../_static/overview/overview_sensors_instanceID.png
+.. figure:: ../../_static/overview/overview_sensors_instanceID.jpg
     :align: center
     :figwidth: 100%
     :alt: A scene captured in RGB
@@ -144,7 +144,7 @@ The main difference between ``instance_id_segmentation_fast`` and ``instance_seg
 Instance Segmentation
 """""""""""""""""""""
 
-.. figure:: ../../_static/overview/overview_sensors_instance.png
+.. figure:: ../../_static/overview/overview_sensors_instance.jpg
     :align: center
     :figwidth: 100%
     :alt: A scene captured in RGB

docs/source/overview/sensors/contact_sensor.rst

@@ -3,7 +3,7 @@
 Contact Sensor
 ================
 
-.. figure:: ../../_static/overview/overview_sensors_contact_diagram.png
+.. figure:: ../../_static/overview/overview_sensors_contact_diagram.jpg
     :align: center
     :figwidth: 100%
     :alt: A contact sensor with filtering

docs/source/overview/sensors/frame_transformer.rst

@@ -3,7 +3,7 @@
 Frame Transformer
 ====================
 
-.. figure:: ../../_static/overview/overview_sensors_frame_transformer.png
+.. figure:: ../../_static/overview/overview_sensors_frame_transformer.jpg
     :align: center
     :figwidth: 100%
     :alt: A diagram outlining the basic geometry of frame transformations
@@ -61,7 +61,7 @@ Let's take a look at the result for tracking specific objects. First, we can tak
   relative orientations: tensor([[[ 0.9623,  0.0072, -0.2717, -0.0020],
           [ 0.9639,  0.0052, -0.2663, -0.0014]]], device='cuda:0')
 
-.. figure:: ../../_static/overview/overview_sensors_ft_visualizer.png
+.. figure:: ../../_static/overview/overview_sensors_ft_visualizer.jpg
     :align: center
     :figwidth: 100%
     :alt: The frame transformer visualizer

docs/source/overview/sensors/imu.rst

+.. _overview_sensors_imu:
+
+Inertial Measurement Unit (IMU)
+===================================
+
+.. figure:: ../../_static/overview/overview_sensors_imu_diagram.jpg
+    :align: center
+    :figwidth: 100%
+    :alt: A diagram outlining the basic force relationships for the IMU sensor
+
+Inertial Measurement Units (IMUs) are a type of sensor for measuring the acceleration of an object.  These sensors are traditionally designed report linear accelerations and angular velocities, and function on similar principles to that of a digital scale: They report accelerations derived from **net force acting on the sensor**.
+
+A naive implementation of an IMU would report a negative acceleration due to gravity while the sensor is at rest in some local gravitational field. This is not generally needed for most practical applications, and so most real IMU sensors often include a **gravity bias** and assume that the device is operating on the surface of the Earth.  The IMU we provide in Isaac Lab includes a similar bias term, which defaults to +g.  This means that if you add an IMU to your simulation, and do not change this bias term, you will detect an acceleration of :math:`+ 9.81 m/s^{2}` anti-parallel to gravity acceleration.
+
+Consider a simple environment with an Anymal Quadruped equipped with an IMU on each of its two front feet.
+
+.. literalinclude:: ../../../../source/standalone/demos/sensors/imu_sensor.py
+  :language: python
+  :lines: 39-63
+
+Here we have explicitly removed the bias from one of the sensors, and we can see how this affects the reported values by visualizing the sensor when we run the sample script
+
+.. figure:: ../../_static/overview/overview_sensors_imu_visualizer.jpg
+    :align: center
+    :figwidth: 100%
+    :alt: IMU visualized
+
+Notice that the right front foot explicitly has a bias of (0,0,0).  In the visualization, you should see that the arrow indicating the acceleration from the right IMU rapidly changes over time, while the arrow visualizing the left IMU points constantly along the vertical axis.
+
+Retrieving values form the sensor is done in the usual way
+
+.. code-block:: python
+
+  def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene):
+    .
+    .
+    .
+    # Simulate physics
+    while simulation_app.is_running():
+      .
+      .
+      .
+      # print information from the sensors
+      print("-------------------------------")
+      print(scene["imu_LF"])
+      print("Received linear velocity: ", scene["imu_LF"].data.lin_vel_b)
+      print("Received angular velocity: ", scene["imu_LF"].data.ang_vel_b)
+      print("Received linear acceleration: ", scene["imu_LF"].data.lin_acc_b)
+      print("Received angular acceleration: ", scene["imu_LF"].data.ang_acc_b)
+      print("-------------------------------")
+      print(scene["imu_RF"])
+      print("Received linear velocity: ", scene["imu_RF"].data.lin_vel_b)
+      print("Received angular velocity: ", scene["imu_RF"].data.ang_vel_b)
+      print("Received linear acceleration: ", scene["imu_RF"].data.lin_acc_b)
+      print("Received angular acceleration: ", scene["imu_RF"].data.ang_acc_b)
+
+The oscillations in the values reported by the sensor are a direct result of of how the sensor calculates the acceleration, which is through a finite difference approximation between adjacent ground truth velocity values as reported by the sim.  We can see this in the reported result (pay attention to the **linear acceleration**) because the acceleration from the right foot is small, but explicitly zero.
+
+.. code-block:: bash
+
+  Imu sensor @ '/World/envs/env_.*/Robot/LF_FOOT':
+          view type         : <class 'omni.physics.tensors.impl.api.RigidBodyView'>
+          update period (s) : 0.0
+          number of sensors : 1
+
+  Received linear velocity:  tensor([[ 0.0203, -0.0054,  0.0380]], device='cuda:0')
+  Received angular velocity:  tensor([[-0.0104, -0.1189,  0.0080]], device='cuda:0')
+  Received linear acceleration:  tensor([[ 4.8344, -0.0205,  8.5305]], device='cuda:0')
+  Received angular acceleration:  tensor([[-0.0389, -0.0262, -0.0045]], device='cuda:0')
+  -------------------------------
+  Imu sensor @ '/World/envs/env_.*/Robot/RF_FOOT':
+          view type         : <class 'omni.physics.tensors.impl.api.RigidBodyView'>
+          update period (s) : 0.0
+          number of sensors : 1
+
+  Received linear velocity:  tensor([[0.0244, 0.0077, 0.0431]], device='cuda:0')
+  Received angular velocity:  tensor([[ 0.0122, -0.1360, -0.0042]], device='cuda:0')
+  Received linear acceleration:  tensor([[-0.0018,  0.0010, -0.0032]], device='cuda:0')
+  Received angular acceleration:  tensor([[-0.0373, -0.0050, -0.0053]], device='cuda:0')
+  -------------------------------
+
+.. dropdown:: Code for imu_sensor.py
+   :icon: code
+
+   .. literalinclude:: ../../../../source/standalone/demos/sensors/imu_sensor.py
+      :language: python
+      :linenos:

docs/source/overview/sensors/index.rst

@@ -17,4 +17,5 @@ The following pages describe the available sensors in more detail:
     camera
     contact_sensor
     frame_transformer
+    imu
     ray_caster

docs/source/overview/sensors/ray_caster.rst

@@ -3,7 +3,7 @@
 Ray Caster
 =============
 
-.. figure:: ../../_static/overview/overview_sensors_rc_patterns.png
+.. figure:: ../../_static/overview/overview_sensors_rc_patterns.jpg
     :align: center
     :figwidth: 100%
     :alt: A diagram outlining the basic geometry of frame transformations
@@ -20,7 +20,7 @@ Using a ray caster sensor requires a **pattern** and a parent xform to be attach
 
 Notice that the units on the pattern config is in degrees! Also, we enable visualization here to explicitly show the pattern in the rendering, but this is not required and should be disabled for performance tuning.
 
-.. figure:: ../../_static/overview/overview_sensors_rc_visualizer.png
+.. figure:: ../../_static/overview/overview_sensors_rc_visualizer.jpg
     :align: center
     :figwidth: 100%
     :alt: Lidar Pattern visualized

source/standalone/demos/sensors/imu_sensor.py

+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""Launch Isaac Sim Simulator first."""
+
+import argparse
+
+from omni.isaac.lab.app import AppLauncher
+
+# add argparse arguments
+parser = argparse.ArgumentParser(description="Tutorial on adding sensors on a robot.")
+parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to spawn.")
+# append AppLauncher cli args
+AppLauncher.add_app_launcher_args(parser)
+# parse the arguments
+args_cli = parser.parse_args()
+
+# launch omniverse app
+app_launcher = AppLauncher(args_cli)
+simulation_app = app_launcher.app
+
+"""Rest everything follows."""
+
+import torch
+
+import omni.isaac.lab.sim as sim_utils
+from omni.isaac.lab.assets import AssetBaseCfg
+from omni.isaac.lab.scene import InteractiveScene, InteractiveSceneCfg
+from omni.isaac.lab.sensors import ImuCfg
+from omni.isaac.lab.utils import configclass
+
+##
+# Pre-defined configs
+##
+from omni.isaac.lab_assets.anymal import ANYMAL_C_CFG  # isort: skip
+
+
+@configclass
+class ImuSensorSceneCfg(InteractiveSceneCfg):
+    """Design the scene with sensors on the robot."""
+
+    # ground plane
+    ground = AssetBaseCfg(prim_path="/World/defaultGroundPlane", spawn=sim_utils.GroundPlaneCfg())
+
+    # lights
+    dome_light = AssetBaseCfg(
+        prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75))
+    )
+
+    # robot
+    robot = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
+
+    imu_RF = ImuCfg(prim_path="{ENV_REGEX_NS}/Robot/LF_FOOT", debug_vis=True)
+
+    imu_LF = ImuCfg(prim_path="{ENV_REGEX_NS}/Robot/RF_FOOT", gravity_bias=(0, 0, 0), debug_vis=True)
+
+
+def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene):
+    """Run the simulator."""
+    # Define simulation stepping
+    sim_dt = sim.get_physics_dt()
+    sim_time = 0.0
+    count = 0
+
+    # Simulate physics
+    while simulation_app.is_running():
+
+        if count % 500 == 0:
+            # reset counter
+            count = 0
+            # reset the scene entities
+            # root state
+            # we offset the root state by the origin since the states are written in simulation world frame
+            # if this is not done, then the robots will be spawned at the (0, 0, 0) of the simulation world
+            root_state = scene["robot"].data.default_root_state.clone()
+            root_state[:, :3] += scene.env_origins
+            scene["robot"].write_root_link_pose_to_sim(root_state[:, :7])
+            scene["robot"].write_root_com_velocity_to_sim(root_state[:, 7:])
+            # set joint positions with some noise
+            joint_pos, joint_vel = (
+                scene["robot"].data.default_joint_pos.clone(),
+                scene["robot"].data.default_joint_vel.clone(),
+            )
+            joint_pos += torch.rand_like(joint_pos) * 0.1
+            scene["robot"].write_joint_state_to_sim(joint_pos, joint_vel)
+            # clear internal buffers
+            scene.reset()
+            print("[INFO]: Resetting robot state...")
+        # Apply default actions to the robot
+        # -- generate actions/commands
+        targets = scene["robot"].data.default_joint_pos
+        # -- apply action to the robot
+        scene["robot"].set_joint_position_target(targets)
+        # -- write data to sim
+        scene.write_data_to_sim()
+        # perform step
+        sim.step()
+        # update sim-time
+        sim_time += sim_dt
+        count += 1
+        # update buffers
+        scene.update(sim_dt)
+
+        # print information from the sensors
+        print("-------------------------------")
+        print(scene["imu_LF"])
+        print("Received linear velocity: ", scene["imu_LF"].data.lin_vel_b)
+        print("Received angular velocity: ", scene["imu_LF"].data.ang_vel_b)
+        print("Received linear acceleration: ", scene["imu_LF"].data.lin_acc_b)
+        print("Received angular acceleration: ", scene["imu_LF"].data.ang_acc_b)
+        print("-------------------------------")
+        print(scene["imu_RF"])
+        print("Received linear velocity: ", scene["imu_RF"].data.lin_vel_b)
+        print("Received angular velocity: ", scene["imu_RF"].data.ang_vel_b)
+        print("Received linear acceleration: ", scene["imu_RF"].data.lin_acc_b)
+        print("Received angular acceleration: ", scene["imu_RF"].data.ang_acc_b)
+
+
+def main():
+    """Main function."""
+
+    # Initialize the simulation context
+    sim_cfg = sim_utils.SimulationCfg(dt=0.005, device=args_cli.device)
+    sim = sim_utils.SimulationContext(sim_cfg)
+    # Set main camera
+    sim.set_camera_view(eye=[3.5, 3.5, 3.5], target=[0.0, 0.0, 0.0])
+    # design scene
+    scene_cfg = ImuSensorSceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0)
+    scene = InteractiveScene(scene_cfg)
+    # Play the simulator
+    sim.reset()
+    # Now we are ready!
+    print("[INFO]: Setup complete...")
+    # Run the simulator
+    run_simulator(sim, scene)
+
+
+if __name__ == "__main__":
+    # run the main function
+    main()
+    # close sim app
+    simulation_app.close()