Localisation mit Partikel Filter

landmark-based localization
1. initial position

We use the GPS to locate the initial position of the car. Since GPS is not accurate, we sample 10 locations (particles) based on the measurement noise specification provided by the GPS vendor. 

 

2. next postion

For each particle, we apply a dynamic model (like x’ = x + v 𝛿t) to calculate the next position at time t + 𝛿t.

 

 

 

To simulate the process noise caused by factors like weather and the road conditions, we add independent random noise to each particle’s location.

 

 

 

3. compute likelihood

With a pre-determined map, we find all the surrounding landmarks.

 

 

 

 

Each particle represents the suggested location of the car. We want to compute the likeliness using measurements from our sensors (like camera and LiDAR).

 

 

We assume the measurement noise is Gaussian distributed.

 

 

We repeat the calculation (PDF) for every new measurement we received. Then the final likelihood for a particle is computed by multiplying all the corresponding PDFs together.

 

 

4. nomralize and compute the weights
compute the location of 10 new particles and repeat the oben process again.

 

https://jonathan-hui.medium.com/tracking-a-self-driving-car-with-particle-filter-ef61f622a3e9