The grid-based tracker (trackerGridRFS) estimates a local or ego-centric dynamic occupancy grid map i.e the dynamic occupancy grid map is always aligned with the current position and orientation of the ego vehicle. In order to estimate the dynamic grid map from sensor-level measurements, the tracker needs mainly two transforms. The first transform is required to account for position and orientation of the sensor with respect to the ego vehicle or grid. The second transform is required to account for position and orientation of the ego vehicle with respect to the world or scenario frame.
In the example, this input is calculated by helper function helperGetLidarConfig provided here in the example. The helper uses the ground truth information about the ego vehicle to calculate this information. In real-world systems, this information about ego position and orientation is typically obtained by INS filters.
The tracker does the following for motion compensation:
- The tracker estimates the grid using a particle filter. The states of the particles are represented in the world coordinate frame (thus allowing state estimation in a global sense).
- The tracker projects the particles to the local grid using the ego-to-scenario transformation.
- The tracker projects the sensor data to the local grid using the sensor-to-ego transformation.
- Both particle data and sensor data gets fused at the local grid level.
Hope this helps.