UKF-Sensor-Fusion
说明： 使用无中心卡尔曼滤波器（C++特征）的激光雷达和雷达传感器融合。
(LiDAR and RADAR sensors fusion using Unscented Kalman Filter (C++ Eigen).)
(LiDAR and RADAR sensors fusion using Unscented Kalman Filter (C++ Eigen).)
文件列表:
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# Sensor Fusion using Unscented Kalman Filter
The main goal of the project is to apply Unscented Kalman Filter to fuse data from LiDAR and Radar sensors mounted on a self-driving vehicle (noisy environment). The pipeline is implemented using C++.
For more details, please, check out the [source code directory](https://github.com/wafarag/UKF-Sensor-Fusion/tree/master/src) and the [testing data file](https://github.com/wafarag/UKF-Sensor-Fusion/blob/master/data/obj_pose-laser-radar-synthetic-input.txt).
## Content of this repo
- `scr` a directory with the project code:
- `main.cpp` - reads in data, calls a function to run the Kalman filter, calls a function to calculate RMSE
- `ukf.cpp` - implements everything in the filter: initializes it, defines and calls the predict function for both lidar and radar, defines and calls the update function for both lidar and radar
- `tools.cpp` - a function to calculate RMSE
- `data` a directory with the input and output data files and records of the results
- `build` a directory with object and executable files
## Result
The results of the UKF fusion algorithm for:
* Both [LiDAR and RADAR](https://github.com/wafarag/UKF-Sensor-Fusion/blob/master/data/Accuracy_Result_Both.txt): RMS => [0.0***759, 0.080944, 0.145182, 0.15924].
* For [LiDAR alone](https://github.com/wafarag/UKF-Sensor-Fusion/blob/master/data/Accuracy_Result_Lidar.txt): RMS => [0.161211, 0.1463***, 0.208204, 0.212872].
* For [RADAR alone](https://github.com/wafarag/UKF-Sensor-Fusion/blob/master/data/Accuracy_Result_Radar.txt): RMS => [0.20313, 0.253866, 0.304843, 0.22708].
The main goal of the project is to apply Unscented Kalman Filter to fuse data from LiDAR and Radar sensors mounted on a self-driving vehicle (noisy environment). The pipeline is implemented using C++.
For more details, please, check out the [source code directory](https://github.com/wafarag/UKF-Sensor-Fusion/tree/master/src) and the [testing data file](https://github.com/wafarag/UKF-Sensor-Fusion/blob/master/data/obj_pose-laser-radar-synthetic-input.txt).
## Content of this repo
- `scr` a directory with the project code:
- `main.cpp` - reads in data, calls a function to run the Kalman filter, calls a function to calculate RMSE
- `ukf.cpp` - implements everything in the filter: initializes it, defines and calls the predict function for both lidar and radar, defines and calls the update function for both lidar and radar
- `tools.cpp` - a function to calculate RMSE
- `data` a directory with the input and output data files and records of the results
- `build` a directory with object and executable files
## Result
The results of the UKF fusion algorithm for:
* Both [LiDAR and RADAR](https://github.com/wafarag/UKF-Sensor-Fusion/blob/master/data/Accuracy_Result_Both.txt): RMS => [0.0***759, 0.080944, 0.145182, 0.15924].
* For [LiDAR alone](https://github.com/wafarag/UKF-Sensor-Fusion/blob/master/data/Accuracy_Result_Lidar.txt): RMS => [0.161211, 0.1463***, 0.208204, 0.212872].
* For [RADAR alone](https://github.com/wafarag/UKF-Sensor-Fusion/blob/master/data/Accuracy_Result_Radar.txt): RMS => [0.20313, 0.253866, 0.304843, 0.22708].
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