some change

• Output Bias Random Walk of gyro and acc, which slope is 0.5.
• change output of noise as Continuous-Time form.
• fix the noise parameter of acc, include Q/N/K/R, remove coefficients 60 or 3600 etc.

The main idea is to fit log Allan Variance value using Q / N / B / K / R and different power include -1, -0.5, 0, 0.5, 1.

imu_utils

A ROS package tool to analyze the IMU performance. C++ version of Allan Variance Tool. The figures are drawn by Matlab, in scripts.

Actually, just analyze the Allan Variance for the IMU data. Collect the data while the IMU is Stationary, with a two hours duration.

refrence

Refrence technical report: Allan Variance: Noise Analysis for Gyroscopes, vectornav gyroscope and An introduction to inertial navigation.

Woodman, O.J., 2007. An introduction to inertial navigation (No. UCAM-CL-TR-696). University of Cambridge, Computer Laboratory.

Refrence Matlab code: GyroAllan

IMU Noise Values

Parameter	YAML element	Symbol	Units
Gyroscope "white noise"	gyr_n
Accelerometer "white noise"	acc_n
Gyroscope "bias Instability"	gyr_b
Accelerometer "bias Instability"	acc_b
Gyroscope “random walk”	gyr_bw
Accelerometer “random walk”	acc_bw

• White noise is at tau=1 or N;
• Bias Instability is around the minimum;
• Random walk is at tau=3000 or K

(according to technical report: Allan Variance: Noise Analysis for Gyroscopes)

sample test

• blue : Vi-Sensor, ADIS16448, 200Hz
• red : 3dm-Gx4, 500Hz
• green : DJI-A3, 400Hz
• black : DJI-N3, 400Hz
• circle : xsens-MTI-100, 100Hz

How to build and run?

to build

sudo apt-get install libdw-dev

• download required code_utils;

• put the ROS package imu_utils and code_utils into your workspace, usually named catkin_ws;

• cd to your workspace, build with catkin_make;

to run

• collect the data while the IMU is Stationary, with a two hours duration;

• (or) play rosbag dataset;

 rosbag play -r 200 imu_A3.bag

• roslaunch the rosnode;

roslaunch imu_utils A3.launch

Be careful of your roslaunch file:

<launch>
    <node pkg="imu_utils" type="imu_an" name="imu_an" output="screen">
        <param name="imu_topic" type="string" value= "/djiros/imu"/>
        <param name="imu_name" type="string" value= "A3"/>
        <param name="data_save_path" type="string" value= "$(find imu_utils)/data/"/>
        <param name="max_time_min" type="int" value= "120"/>
        <param name="max_cluster" type="int" value= "100"/>
    </node>
</launch>

sample output:

%YAML:1.0
---
type: IMU
name: simulator
Gyr:
   unit: " rad/s"
   avg-axis:
      gyr_n: 1.4812687142891424e-02
      gyr_b: 9.7998795103765280e-04
      gyr_bw: 4.6509871480460975e-05
   x-axis:
      gyr_n: 1.4692399528685599e-02
      gyr_b: 9.8518793903601973e-04
      gyr_bw: 2.8490000231200879e-05
   y-axis:
      gyr_n: 1.4844275115273257e-02
      gyr_b: 1.0037833725431490e-03
      gyr_bw: 5.8666018195331909e-05
   z-axis:
      gyr_n: 1.4901386784715413e-02
      gyr_b: 9.5099254153378971e-04
      gyr_bw: 5.2373596014850146e-05
Acc:
   unit: " m/s^2"
   avg-axis:
      acc_n: 1.9109749723765068e-02
      acc_b: 3.1248109984496363e-03
      acc_bw: 4.0387852301552378e-04
   x-axis:
      acc_n: 1.9279401330095185e-02
      acc_b: 3.2669662466337092e-03
      acc_bw: 4.4151611213750250e-04
   y-axis:
      acc_n: 1.8992891739389096e-02
      acc_b: 3.1861302761859002e-03
      acc_bw: 4.6298800537015376e-04
   z-axis:
      acc_n: 1.9056956101810924e-02
      acc_b: 2.9213364725292996e-03
      acc_bw: 3.0713145153891502e-04

dataset

DJI A3: 400Hz

Download link: 百度网盘

DJI A3: 400Hz

Download link: 百度网盘

ADIS16448: 200Hz

Download link:百度网盘

3dM-GX4: 500Hz

Download link:百度网盘

xsens-MTI-100: 100Hz

Download link:百度网盘