Inertial Measurement Unit (IMU)

Inertial Measurement Unit (IMU)#

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 \(+ 9.81 m/s^{2}\) anti-parallel to gravity acceleration.

Consider a simple environment with an Anymal Quadruped equipped with an IMU on each of its two front feet.

##
# Pre-defined configs
##
from isaaclab_assets.robots.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)

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

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

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.

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')
-------------------------------
Code for imu_sensor.py
  1# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
  2# All rights reserved.
  3#
  4# SPDX-License-Identifier: BSD-3-Clause
  5
  6# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
  7# All rights reserved.
  8#
  9# SPDX-License-Identifier: BSD-3-Clause
 10
 11"""Launch Isaac Sim Simulator first."""
 12
 13import argparse
 14
 15from isaaclab.app import AppLauncher
 16
 17# add argparse arguments
 18parser = argparse.ArgumentParser(description="Example on using the IMU sensor.")
 19parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to spawn.")
 20# append AppLauncher cli args
 21AppLauncher.add_app_launcher_args(parser)
 22# parse the arguments
 23args_cli = parser.parse_args()
 24
 25# launch omniverse app
 26app_launcher = AppLauncher(args_cli)
 27simulation_app = app_launcher.app
 28
 29"""Rest everything follows."""
 30
 31import torch
 32
 33import isaaclab.sim as sim_utils
 34from isaaclab.assets import AssetBaseCfg
 35from isaaclab.scene import InteractiveScene, InteractiveSceneCfg
 36from isaaclab.sensors import ImuCfg
 37from isaaclab.utils import configclass
 38
 39##
 40# Pre-defined configs
 41##
 42from isaaclab_assets.robots.anymal import ANYMAL_C_CFG  # isort: skip
 43
 44
 45@configclass
 46class ImuSensorSceneCfg(InteractiveSceneCfg):
 47    """Design the scene with sensors on the robot."""
 48
 49    # ground plane
 50    ground = AssetBaseCfg(prim_path="/World/defaultGroundPlane", spawn=sim_utils.GroundPlaneCfg())
 51
 52    # lights
 53    dome_light = AssetBaseCfg(
 54        prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75))
 55    )
 56
 57    # robot
 58    robot = ANYMAL_C_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
 59
 60    imu_RF = ImuCfg(prim_path="{ENV_REGEX_NS}/Robot/LF_FOOT", debug_vis=True)
 61
 62    imu_LF = ImuCfg(prim_path="{ENV_REGEX_NS}/Robot/RF_FOOT", gravity_bias=(0, 0, 0), debug_vis=True)
 63
 64
 65def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene):
 66    """Run the simulator."""
 67    # Define simulation stepping
 68    sim_dt = sim.get_physics_dt()
 69    sim_time = 0.0
 70    count = 0
 71
 72    # Simulate physics
 73    while simulation_app.is_running():
 74
 75        if count % 500 == 0:
 76            # reset counter
 77            count = 0
 78            # reset the scene entities
 79            # root state
 80            # we offset the root state by the origin since the states are written in simulation world frame
 81            # if this is not done, then the robots will be spawned at the (0, 0, 0) of the simulation world
 82            root_state = scene["robot"].data.default_root_state.clone()
 83            root_state[:, :3] += scene.env_origins
 84            scene["robot"].write_root_link_pose_to_sim(root_state[:, :7])
 85            scene["robot"].write_root_com_velocity_to_sim(root_state[:, 7:])
 86            # set joint positions with some noise
 87            joint_pos, joint_vel = (
 88                scene["robot"].data.default_joint_pos.clone(),
 89                scene["robot"].data.default_joint_vel.clone(),
 90            )
 91            joint_pos += torch.rand_like(joint_pos) * 0.1
 92            scene["robot"].write_joint_state_to_sim(joint_pos, joint_vel)
 93            # clear internal buffers
 94            scene.reset()
 95            print("[INFO]: Resetting robot state...")
 96        # Apply default actions to the robot
 97        # -- generate actions/commands
 98        targets = scene["robot"].data.default_joint_pos
 99        # -- apply action to the robot
100        scene["robot"].set_joint_position_target(targets)
101        # -- write data to sim
102        scene.write_data_to_sim()
103        # perform step
104        sim.step()
105        # update sim-time
106        sim_time += sim_dt
107        count += 1
108        # update buffers
109        scene.update(sim_dt)
110
111        # print information from the sensors
112        print("-------------------------------")
113        print(scene["imu_LF"])
114        print("Received linear velocity: ", scene["imu_LF"].data.lin_vel_b)
115        print("Received angular velocity: ", scene["imu_LF"].data.ang_vel_b)
116        print("Received linear acceleration: ", scene["imu_LF"].data.lin_acc_b)
117        print("Received angular acceleration: ", scene["imu_LF"].data.ang_acc_b)
118        print("-------------------------------")
119        print(scene["imu_RF"])
120        print("Received linear velocity: ", scene["imu_RF"].data.lin_vel_b)
121        print("Received angular velocity: ", scene["imu_RF"].data.ang_vel_b)
122        print("Received linear acceleration: ", scene["imu_RF"].data.lin_acc_b)
123        print("Received angular acceleration: ", scene["imu_RF"].data.ang_acc_b)
124
125
126def main():
127    """Main function."""
128
129    # Initialize the simulation context
130    sim_cfg = sim_utils.SimulationCfg(dt=0.005, device=args_cli.device)
131    sim = sim_utils.SimulationContext(sim_cfg)
132    # Set main camera
133    sim.set_camera_view(eye=[3.5, 3.5, 3.5], target=[0.0, 0.0, 0.0])
134    # design scene
135    scene_cfg = ImuSensorSceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0)
136    scene = InteractiveScene(scene_cfg)
137    # Play the simulator
138    sim.reset()
139    # Now we are ready!
140    print("[INFO]: Setup complete...")
141    # Run the simulator
142    run_simulator(sim, scene)
143
144
145if __name__ == "__main__":
146    # run the main function
147    main()
148    # close sim app
149    simulation_app.close()