[Multi-sensor fusion] A complete technical learning route

Prepare to open a column dedicated to explaining the integration of Lidar, Radar, and Camera, and systematically learn every part from basic knowledge to advanced operations.

Mainly include:

1. Programming environment
1. ROS foundation
2. Point cloud library PCL foundation
3. Opencv foundation
4. Tenserflow foundation
2. Sensor aspect
1.Lidar basic principle and data form
2. Millimeter wave radar basic principle and data form
3. Camera basic principle
4. Lidar related detection algorithm learning
5. Millimeter wave radar SDK learning
6. Camera detection algorithm learning
3. Matching tracking and fusion
1. Hungarian matchingAlgorithm
2.Kalman Filter
3.SORT algorithm matching tracking
4.Multi-sensor fusion coordinate transformation
5.Data fusion (pre-fusion)
6. Target fusion (post fusion)
4. Data set and simulation software
1. Kitti data set
2. nuScence data set
3. Simulation platform introduction
4. Simulation data re-annotation

The picture below is the skill tree of the entire learning route. In the later stage, I will give special explanations based on the above skills at the frequency of two updates a week, including how to learn each part and getting started with basic knowledge. The above parts will also be gradually completed.The form of link is convenient for everyone to learn, and it is continuously updated.