Likelihood Field Model
- tags
- Map Matching
Key Idea
Project an individual sensor measurement \(z_t^k\) into the global coordinate frame of map \(m\). Discards max-range readings.
Assumes three types of noise, similar to Range Finder Model:
- Measurement noise: Gaussian
- Failures: point-mass distribution at \(z_{\text{max}}\)
- Random measurements: Uniform distribution \(p_{\text{rand}}\)
The model is a mixture of these 3 densities:
\begin{equation} z_{\mathrm{hit}} \cdot p_{\mathrm{hit}}+z_{\mathrm{rand}} \cdot p_{\mathrm{rand}}+z_{\mathrm{max}} \cdot p_{\mathrm{max}} \end{equation}
Issues
- Does not explicitly model dynamic objects that cause short readings
- Treats sensors as being able to see through walls: ray casting replaced by nearest neighbour function: incapable of determining whether a path to a point is intercepted by an obstacle in the map
- Does not account for map uncertainty
These issues can be addressed via extensions to the algorithm.