IMU noise modeling

This repository models different stochastic noise sources in an IMU - namely white noise, brown noise and pink noise. It implements montecarlo simulations to understand the position errors generated from each of these noise sources for the IMU parameters obtained from AD curves (stationary data collected greater than 5hrs). The position error is computed by dead-reckoning using rk method.

The repository also has verification scripts to confirm the errors of the model and check the accuracy in modeling of each noise source.

Note: In future we will also add bulk model parameters and their effect on the overall error growth when an IMU is used.

Important submodules:

1. config_files (local gitlab instance): Different branches of the config files store different IMUs' noise values.
2. some public github projects: which have done similar work.

Todo log:

• [✓] configure the simulation for pink noise senstivity analysis and check the parameters being set are right.
• [✓] Delete tau in the configuration files of pink simulation as well as from the code.
• [✓] setup new functions in the code for running pink noise simulations.
• [✓] run the senstivity analysis - simulation in parallel in hulk or crunch.
• [] setup simulation configs for each sensor by removing the folder name in the config files and editing the mc_sim_config.
  • [✓] - vn100 -
  • [✓] - voxl
  • [✓] - xsense
  • [] - TUM - BMI160
  • [] - EUROC - ADIS16448
• [] Simulations:
  • [] - vn100 → w - , b- , p- , wbp- , wb-
  • [] - icp42688p → w - ✗ , b - ✗, p - ✗ , wbp_ag - ✗ , wb_ag - ✗
  • [] - xsens → w - , b - , p - ✗ , wbp_ag - ✗ , wb_ag -
  • [] - TUM → w- , b- , p- , wbp- , wb-
  • [] - integration of stationary data vs simulated model - replication of the behavior for TUM dataset.
  • [] - comparison of wb and wbp of xsens and vn100 imu.
  • [] - experimental dead-reckoning - error compilation from all flights. Remove arbitrary functions.
• [] edit the config files for different sensors and check the values with AD curves.
• [] Run simulations for different sensors and record their results.
• [] Improve visualization scripts for plotting all sensors together.

Resources

1. Github repository with important modeling hints
2. IEEE standard on fiber optic gyros - v2
3. [IEEE standard on fibler optic gyros
   • v1](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=660628&tag=1)

Script usage

A collection of scripts to simulate and validate IMU noise models.

Running the code

On MATLAB command line, run the following:

Data to use

Configs to set

All Scripts

The groups of scripts are described below

Scripts for doing all the tasks by selecting the task

script_paths

main

Scripts for AD curves and saving them

save_public_datasets_to_std_struct

experimental_AD_curves

experimental_AD_curves_TUM

Scripts for comparing and model verification

inflated_vs_actual_AD_comparison

theoretical_AD_curves

sensor_model_verification

Monte-Carlo Simulation scripts

process_mc_sim_logs

MonteCarloSim_w

MonteCarloSim_wb

MonteCarloSim_wbp

MonteCarloSim_wb_ag

Visualization

Plots

Experimental evaluation with ground truth

dead_reckoning_euroc

To compute Allan Deviation curves:

Example setup

VOXL data processing

VOXL data is stored in the following way:

+-- home
   +-- datasets
       +-- vio_calibration_all_setups
           +-- voxl_deck
               +--- imu0_params.yaml
               +--- vn100_params.yaml
               +--- april_grid_params.yaml
               +--- voxl_calibration_flight<id1-id2>.bag
       +-- voxl_m500_logs
          +-- flight1 (has rosbag, gt data and euroc format IMU data)
              +--Optitrack_data
                 +-- take_file_optitrack.csv (from optitrack)
              +--voxl_run_flight1_<date>.bag (obtained during flight log, is modified with a script)
              +--qvio_log_run_flight1.txt (obtained during flight log)
              +--data_imu0.txt (EUROC format style file, after data collect scripts)
              +--data_vn100_modified_time.txt (converted to voxl time, after data collect scripts)

          +-- flight2
          .
          .
          +-- flightN

          +-- vio_results
              +-- flight1
                  +--gt_raw_flight1_<date>.csv (optitrack frame, utc time)
                  +--gt_W_flight1_time_offset.csv (World frame - ROS convention, voxl time)
                  +--vinsmono_flight1_imu0.csv (generated from vinsmono, imu0 parameters)
                  +--vinsmono_result_flight1_vn100.csv (generated from vinsmono, vn100 parameters)
                  +--imu0_euroc.csv
                  +--vn100_euroc.csv
                  +--voxl_run_flight1_<date>.mat (obtained from bag2mat tool and related config file)

              +-- flight2
              .
              .
              +-- flightN
          |

How to obtain each one of these files.