Abstract: As for efficient working of autonomous systems, the system needs to take input data from various sensors like RADAR, LIDAR, Camera and for each sensor mentioned they have got their advantages and disadvantages, and each sensor has noise content in the data provided by sensor. So, it is better to use Kalman filters, as they are really good at taking noisy sensor data and smoothing out the data to make more accurate predictions. For autonomous vehicles, Kalman filters can be used in object tracking. The most interesting benefits of Kalman filters is that they can give us insights into variables that we cannot directly measured. Although lidar does not directly give velocity information, the Kalman filter can infer velocity from the lidar measurements.
The pipeline for the project:
- Simulated data generation for lidar.
- Visualizing data.
- Using Kalman Filter.
- Velocity calculation & visualization using Kalman filters.