Test Tasks

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Area: IMU Data Processing

About

"An Inertial Measurement Unit (IMU) is an electronic device that measures and reports on a craft's velocity, orientation, and gravitational forces, using a combination of accelerometers and gyroscopes, sometimes also magnetometers..." © Wikipedia

We use ADIS16375 (fully compatible with ADIS16488) with a tri-axis gyroscope and a tri-axis accelerometer.

IMU, ADIS16375 in NC353L-369 camera
IMU in Eyesis4π
Fig.3 Positive rotation angles in the calibration machine
Fig.4 Goniometer
Fig.5 IMU acceleration data during the Eyesis4Pi calibration


Event Logger

Features

  • ~1 micro second precision
  • Up to 4 source channels:
    • IMU
    • GPS
    • Image Acquisition
    • External General Purpose Input (e.g., odometer - 3..5V pulses)

Description

The FPGA-based Event Logger uses local clock for time-stamping, so each log entry (IMU, GPS, Image Acquisition and External Input) is recorded with timing info.


In a single camera each acquired image has a timestamp in its header (Exif). The log entry for images has this timestamp recorded at the logger (local) time.

Multiple cameras (e.g., Eyesis4π) are synchronized by the master camera to sub-microsecond, and each acquired also image has the master timestamp in Exif. The log entries for images (if logged in the camera other than master so with different local clock) have 2 fields - master timestamp (same as in image Exif) and local timestamp (same clock as used for IMU), so it is easy to match images with inertial data.


A typical log record has the following format: 

[LocalTimeStamp] [SensorData]
Examples or parsed records:
[LocalTimeStamp]: IMU: [wX] [wY] [wZ]  [dAngleX] [dAngleY] [dAngleZ] [accelX] [accelY] [accelZ] [veloX] [veloY] [veloZ] [temperature]
[LocalTimeStamp]: GPS: [NMEA sentence]
[LocalTimeStamp]: SRC: [MasterTimeStamp]

Syncing with an external device (Eyesis4PI - 10353 based)

An external device (e.g., odometer) can be connected with a camera / camera rig.

The device have to send HTTP requests to be logged to the camera on http://192.168.0.221/imu_setup.php?msg=message_to_log (message is limited to 56 bytes) and 3..5V pulses on the two middle wires of the J15 connector. !! Make sure only the two middle wires are connected and the externals one are not. Since the camera's input trigger is optoisolated, but not the trigger output. !!

For testing purposes we used an Arduino Yún with Adafruit Ultimate GPS Logger Shield. The GPS was used only to send a PPS to the camera's J15 port while Arduino Yún was running a simple script such as: 

echo "" > wifi.log ; i=0; while true; do wget http://192.168.0.221/imu_setup.php?msg=$i -O /dev/null -o /dev/null; echo $i ; echo $i >> wifi.log ; iwlist wlan0 scan >> wifi.log ; i=`expr $i + 1`; done

This script is logging an incrementing number both to the camera log and Yún's file system, WiFi scanning is also recorded to the Yún's log. So later both log files can be synchronized in post-processing.

How to record log on the camera

over network

(see http://192.168.0.9/logger_launcher.php source for options and details): 

mount_point=/absolute_path - the path at which the storage is mounted (usb or nfs)

from command line (10393 only)

  • START:
mkdir /www/pages/logs
cat /dev/imu > /www/pages/logs/test.log
  • STOP:
CTRL-C or killall cat

View recorded logs (read_imu_log.php)

from 10393s camera

http://192.168.0.9/read_imu_log.php

  • looks for logs in /www/pages/logs (automatically created on the first access)
  • displays and filters messages - EXT, GPS (NMEA GPVTG, GPGSA, GPGGA, GPRMC), IMU, IMG (4 ports)
  • can convert a log to a CSV file (is saved to PC)

Notes

  • GPS data is written to both, the event log and the image header (Exif).
  • IMU data is written only to the event log.
  • Example IMU (ADIS16375) samples rate is 2460Hz.
  • Example GPS receiver (Garmin 18x serial) samples rate is 5Hz in NMEA or other configured format.

Examples

Raw

Raw *.log files are found here

Parsed

parsed_log_example.txt (41.3MB) - here

Tools for parsing logs

Download one of the raw logs.


Our projects


1. Images Relative Orientation

The goal is to find how the camera rotated/moved (Rotation Matrix) between the shots. Knowing the images' relative orientation allows to use this information for further 3D reconstruction.

2. ERS Correction

Our sensor is Aptina's MT9P006 - CMOS, 5MP, 1/2.5". It has an ERS (Electronic Rolling Shutter) - the image scan time is about 1/15 s (~66 ms) while IMU samples data at 2.460 kHz (T = 0.406 ms) which makes it possible to reconstruct how the camera moved during the image acquisition and correct the ERS effect by placing each pixel according to the sensor orientation at each moment of time. This only corrects the movement/rotation of the camera but not the ERS effect from the fast moving objects.



Test Tasks


Input Data

timestamp - timestamp of an image set taken at certain camera orientation during the calibration process. Seconds since January 1, 1970, the Time Zone is GMT
axial - angle in degrees about Z-axis (Fig.3)
tilt - angle in degrees about Y-axis (Fig.3)
Motor2 - motor 2 state in its steps
Motor3 - motor 3 state in its steps
NumPoints - applies to images only
RMS - applies to images only

A log file contains 3 types of records (events):
  a. (2.460kHz) IMU data
      - [localTimeStamp,GMT]: IMU:[gyroX][gyroY][gyroZ][angleX][angleY][angleZ][accelX][accelY][accelZ][veloX][veloY][veloZ][temperature]
      where original parameters are angular velocities and accelerations - delta angles and delta velocities represent an integration of original parameters.
  b. (5Hz) GPS data
      - [localTimeStamp,GMT]: GPS: $GPRMC,231112.2,A,4043.36963,N,11155.90616,W,000.00,089.0,250811,013.2,E
  c. (rare) Image-acquired event registered at local clock. 
      - [localTimeStamp,GMT]: SRC: [masterTimeStamp,GMT][localTimeStamp,GMT],
      knowing the master's timestamp is important for matching the events with the taken images - each image has a master timestamp in its EXIF header. 
      The local timestamp is just the time at which an event was registered.



Requirements

  • Programming language - Java



Task 1. Parse the IMU Data

Description
  • The IMU model is ADIS16375.
  • Learn how the log data parser works from the reference Java program.
  • Modify or write your own parser and plot the IMU data in a spreadsheet as Value(Time) (for accelerations and angular velocities) or Sum(Time) (for delta angles and delta velocities) for a given log file.
Comments

-

Output

A spreadsheet with plotted data.


Task 2. Estimating biases (zero drifts) of the IMU Data

Description
  • Determine the time intervals of when the camera is at rest.
  • Estimate the average biases (zero drifts) of ADIS16375 IMU output data when at rest. The formulas can be retrieved from the reference program.
Comments
  • The Camera-IMU system can be considered at rest within some time range from the image registration event - SRC-record in the log file. This range is also to be determined/estimated.
  • The first SRC-record is found in test.log-00002.
Output

Estimated zero drifts stored in a public variable and in an XML file.


Task 3. Calculating the IMU parameters more precisely

Description
Comments
  • Calculate the relative IMU rotation matrices for each state at which the pictures were acquired considering the calibration machine angles to be precise enough.
  • Knowledge of the precise initial IMU orientation is not needed - can be defined arbitrarily.
  • The first SRC-record is found in test.log-00001.
  • Max records in a log file 10,000,000.
Output

Calculated with a better precision IMU parameters in a public variable and in an XML file.

Links

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