|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Multi_sensor_fusion||675||24 days ago||2||gpl-3.0||C++|
|Multi-Sensor Fusion (GNSS, IMU, Camera) 多源多传感器融合定位 GPS/INS组合导航 PPP/INS紧组合|
|Navego||473||5 months ago||9||other||MATLAB|
|NaveGo: an open-source MATLAB/GNU Octave toolbox for processing integrated navigation systems and performing inertial sensors analysis.|
|Ridi_imu||66||4 years ago||3||mit||C++|
|Rome.jl||58||10 days ago||42||mit||Julia|
|Robot Motion Estimate: Tools, Variables, and Factors for SLAM in robotics; also see Caesar.jl.|
|Agrobot||32||3 months ago||bsd-3-clause||Jupyter Notebook|
|Neural-Kalman GNSS/INS Navigation for Precision Agriculture|
|Platform Aceinna_imu||31||10 months ago||12||Python|
|Aceinna Navigation Studio: open-source, embedded development platform for Aceinna IMU hardware. Run custom algorithms and navigation code on Aceinna IMU/INS hardware|
|Wiln||19||3 months ago||15||Jupyter Notebook|
|A lidar-based Teach-and-Repeat framework designed to enable outdoor autonomous navigation in harsh weather.|
|Jetbot_diff_drive||14||3 years ago|
||| A ROS package of the WaveShare Jetbot AI Kit. A Differential Drive Robot |||
|Lidarbot||11||a day ago||bsd-3-clause||Python|
|A differential drive robot is controlled using ROS2 Humble running on a Raspberry Pi 4 (running Ubuntu server 22.04). The vehicle is equipped with a raspberry pi camera for visual feedback and an RPLIDAR A1 sensor used for Simultaneous Localization and Mapping (SLAM), autonomous navigation and obstacle avoidance.|
|Rins W||9||3 years ago||mit||Python|
|RINS-W: Robust Inertial Navigation System on Wheels|
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. 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.
Here 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.
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.
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:
Note: The files having "(No Mag)" in there names, don't contain magnetometer data.
For the purpose of this project, I used the following models:
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)