https://github.com/banaankiamanesh/dl-for-navigation
Implementation of Regression Models on Navigation with IMUs.
https://github.com/banaankiamanesh/dl-for-navigation
9-axis-imu ahrs bno055 bosch cpp datacollection deeplearning imu knn-regression navigation neural-networks python python3 raspberry-pi recurrent-neural-network recurrent-neural-networks regression-models support-vector-regression tensorflow2
Last synced: about 1 year ago
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Implementation of Regression Models on Navigation with IMUs.
- Host: GitHub
- URL: https://github.com/banaankiamanesh/dl-for-navigation
- Owner: BanaanKiamanesh
- License: mit
- Created: 2022-02-25T08:07:16.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-08-23T14:22:13.000Z (almost 4 years ago)
- Last Synced: 2025-03-26T20:51:29.495Z (about 1 year ago)
- Topics: 9-axis-imu, ahrs, bno055, bosch, cpp, datacollection, deeplearning, imu, knn-regression, navigation, neural-networks, python, python3, raspberry-pi, recurrent-neural-network, recurrent-neural-networks, regression-models, support-vector-regression, tensorflow2
- Language: Python
- Homepage:
- Size: 14.3 MB
- Stars: 15
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# **Implementation of Regression Models(DRNN + SVR + KNR) Against Kalman Filters for the Calculation of Quaternions and Euler Angles**
### This Repo Contains 2 Main Parts:
* [Dataset](https://github.com/BanaanKiamanesh/DL_for_Navigation/tree/main/DataSet)
* 1. [DRNN Model](https://github.com/BanaanKiamanesh/DL_for_Navigation/tree/main/RNN%20Model)
2. [SVR+KNR Model](https://github.com/BanaanKiamanesh/DL_for_Navigation/tree/main/SVR%20Model)
## DataSet Collection
#### Data Contains
1. Gyroscope Data 3DoF
2. Accelerometer Data 3DoF
3. Magnetometer Data 3DoF
4. Euler Angles Data 3DoF
5. Quaternions Data 4DoF
To collect the data, I made a small setup using a Raspberry pi and BNO055 IMU. So simple but it works wonderfully.
### 1. Hardware Setup
1. Raspberry pi 4
2. BNO055 9DoF IMU
3. Lithium Polymer Battery
4. Buck Converter
5. USB-C to USB-A Adapter
6. Breadboard
7. Bunch of Jumper Wires
**Note:** It should be portable, So the data collection is not constrained to a specific location and angle.
---
### 2. Software Setup
[Here](https://github.com/BanaanKiamanesh/DL_for_Navigation/tree/main/Data_Collection) is the code for the data collection.
First, the code is written in C++. The code contains a loop with a constant frequency of 100Hz. The loop reads the IMU data for 5 minutes and writes it to a ".csv" file at each iteration.
There is a Executable file for the Code in the same folder.
But in case you want to run the code, you need to install the WiringPi library from [here](http://wiringpi.com/download-and-install/).
There is another prerequisite for the code to run.
High speed I2C (400kHz) in the used Raspberry pi 4 have to be anabled.
For the purpose use the procedure described in the [here](https://www.raspberrypi-spy.co.uk/2018/02/change-raspberry-pi-i2c-bus-speed/).
After handling all, the code is ready to be compiled.
So, Navigate to the folder where the code is and ***run*** the following ***bash*** command:
$ g++ -o CollectorApp *.cpp -lwiringPi -lm -std=c++11
***Now the executable file is ready to be run like this***:
$ ./CollectorApp
---
#### **There are 8 Available Files Containing Data in the *"DataSet"* Folder.**
> **Note:** The files having "(No Mag)" in there names, don't contain magnetometer data.
---
## **Models**
For the purpose of this project, I used the following models:
* Deep-RNN Model
* Support-Vector-Regression Model
* K-Neighbours-Regression Model
Seems Like the Complexity of the data is so much that the ***RNN Model*** is not able to handle it.
And it had a huge RMS of ***0.5*** in it's predictions and also the model stopped learning after a few epochs. Although the learning rate was adaptively adjusted.
So, I decided to use the ***Multi Output Support Vector Regression*** model.
Which seemed to handle the data better.
But, also the Model didn't perform well enough!
So, as a last resort, I used the ***K-Neighbours Regression*** model. Which turned out to be the finest model for this data...
And with a bit of tuning, it was able to perform the best on the data.
with a mean absolute error of ***0.0043*** for Quaternions and ***3.3*** for Euler Angles.
> (**Note:** The mean absolute error is the average of the absolute errors of the predictions)
---
#### Logs for all the models are saved in a file named "Logs.txt" in the specific folder of each model.