基于激光雷达和毫米波雷达数据融合基于无迹卡尔曼滤波算法c++源码+文档说明

# SensorFusion-UKF 激光雷达和毫米波雷达数据融合基于无迹卡尔曼滤波算法c++工程项目 # 基于无迹卡尔曼滤波，改成ROS协议下的 #你需要配置ROS环境以及C++编译 # Unscented Kalman Filter Project Starter Code Self-Driving Car Engineer Nanodegree Program --- ## Dependencies * cmake >= v3.5 * make >= v4.1 * gcc/g++ >= v5.4 ## Basic Build Instructions 1. Clone this repo. 2. Make a build directory: `mkdir build && cd build` 3. Compile: `cmake .. && make` 4. Run it: `./UnscentedKF path/to/input.txt path/to/output.txt`. You can find some sample inputs in 'data/'. - eg. `./UnscentedKF ../data/sample-laser-radar-measurement-data-1.txt output.txt` ## Editor Settings We've purposefully kept editor configuration files out of this repo in order to keep it as simple and environment agnostic as possible. However, we recommend using the following settings: * indent using spaces * set tab width to 2 spaces (keeps the matrices in source code aligned) ## Code Style Please stick to [Google's C++ style guide](https://google.github.io/styleguide/cppguide.html) as much as possible. ## Generating Additional Data This is optional! If you'd like to generate your own radar and lidar data, see the [utilities repo](https://github.com/udacity/CarND-Mercedes-SF-Utilities) for Matlab scripts that can generate additional data. ## Project Instructions and Rubric This information is only accessible by people who are already enrolled in Term 2 of CarND. If you are enrolled, see [the project page](https://classroom.udacity.com/nanodegrees/nd013/parts/40f38239-66b6-46ec-ae68-03afd8a601c8/modules/0949fca6-b379-42af-a919-ee50aa304e6a/lessons/c3eb3583-17b2-4d83-abf7-d852ae1b9fff/concepts/4d0420af-0527-4c9f-a5cd-56ee0fe4f09e) for instructions and the project rubric.