imt-ring 1.6.34

RING: Recurrent Inertial Graph-based Estimator

Recurrent Inertial Graph-based Estimator (RING)

ℹ️ Tip:

Check out my new plug-and-play interface for inertial motion tracking (RING included) here.

Installation

Supports Python=3.10/3.11/3.12 (tested).

Install with pip using

pip install imt-ring

Typically, this will install jax as cpu-only version. Afterwards, gpu-enabled version can be installed with

pip install --upgrade "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

Documentation

Available here.

Quickstart Example

import ring
import numpy as np

T  : int       = 30        # sequence length     [s]
Ts : float     = 0.01      # sampling interval   [s]
B  : int       = 1         # batch size
lam: list[int] = [0, 1, 2] # parent array
N  : int       = len(lam)  # number of bodies
T_i: int       = int(T/Ts) # number of timesteps

X              = np.zeros((B, T_i, N, 9))
# where X is structured as follows:
# X[..., :3]   = acc
# X[..., 3:6]  = gyr
# X[..., 6:9]  = jointaxis

# let's assume we have an IMU on each outer segment of the
# three-segment kinematic chain
X[..., 0, :3]  = acc_segment1
X[..., 2, :3]  = acc_segment3
X[..., 0, 3:6] = gyr_segment1
X[..., 2, 3:6] = gyr_segment3

ringnet = ring.RING(lam, Ts)
yhat, _ = ringnet.apply(X)
# yhat: unit quaternions, shape = (B, T_i, N, 4)

Known fixes

Offscreen rendering with Mujoco

mujoco.FatalError: an OpenGL platform library has not been loaded into this process, this most likely means that a valid OpenGL context has not been created before mjr_makeContext was called

Solution:

import os
os.environ["MUJOCO_GL"] = "egl"

Windows-related: ImportError: DLL load failed while importing ...

ImportError: DLL load failed while importing _multiarray_umath: Das angegebene Modul wurde nicht gefunden.

Solution:

1. pip uninstall -y jax jaxlib
2. conda install -c conda-forge jax

Publications

The following publications utilize this software library, and refer to it as the Random Chain Motion Generator (RCMG) (more specifically the function ring.RCMG):

• RNN-based Observability Analysis for Magnetometer-Free Sparse Inertial Motion Tracking
• Plug-and-Play Sparse Inertial Motion Tracking With Sim-to-Real Transfer
• RNN-based State and Parameter Estimation for Sparse Magnetometer-free Inertial Motion Tracking

Other useful ressources

Particularly useful is the following publication from Roy Featherstone

• A Beginner’s Guide to 6-D Vectors (Part 2)

Contact

Simon Bachhuber ([e-mail])