Kalman Filter
Components of a Kalman Filter
Term | Rank | Describes | Description |
---|---|---|---|
\(A_t\) | Matrix \((n × n)\) | state change | Describes how the state evolves from \(t - 1\) to \(t\) without controls or noise. |
\(B_t\) | Matrix \((n × l)\) | control change | Describes how the control \(u_t\) changes the state from \(t - 1\) to \(t\). |
\(C_t\) | Matrix \((k × n)\) | maps state to observation | Describes how to map the state \(x_t\), to an observation \(z_t\). |
\(A_t\)
How does the state evolve if no command or control is executed.
\(B_t\)
term | purpose |
---|---|
n | The dimensionality of the state |
l | The dimensionality of the odometry |
|
|
+-----------------+
| measurements |
+-----------------+
+-----------------+
| update step |
+-----------------+
|
|
v
+--------------------------+ +-----------------+
| output estimate of state | <-- | P,x k|k |
+--------------------------+ +-----------------+
|
|
v
+-----------------+
| next timestep |
+-----------------+
|
|
v
+--------------------------+ +-----------------+
| prior knowledge of state | --> | P,x k-1|k-1 |
+--------------------------+ +-----------------+
|
|
v
+-----------------+
| prediction step |
+-----------------+
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