Introduction to sensor fusion

1. Sensor fusion

Uncertainty Sensor has their own pros & con So need to sensor fusion Even sensor fusion it is difficult to figure out world

2. Sensors

In the world, there are a lot of sensor existing like camera lidar radar gps imu, even biologic sensor

3. Lidar

Active sensor Laser sensor FOV 360 degree Vertical field of view(how high, how low) Point cloud(intensity, it used at ML/DL for detecting object)

4. Camera

Passive sensor Understanding night vision High resolution Image only can be read by camera Camera can be worked as active sensor by preprocessing algorisms (intensity, feature based)

5. Radar

Active sensor Signal process Understand potion and velocity Propagate out and get back frequency from unit Low resolution Just can figure where is object like this Cost effective

6. Fusion

Merge sensors Figure out environment using Mathematical Prediction and update cycle(update predict by actual observation data) Sensor often continuous form But we can change discrete form Choose correct sensor

7. Kalman Filters

Linear form

But real world is non-linear (curve or unexpected behavior)

So need to use extended Kalman filter(Taylor 1st series, not long term driving good, because their error is accumulated) or unscented Kalman filter(sampling and correct(like particle filter, long drive advance)