torchfilter.base._virtual_sensor_model

Private module; avoid importing from directly.

Module Contents

Classes

VirtualSensorModel

Virtual sensor base class for our differentiable Kalman filters.

class torchfilter.base._virtual_sensor_model.VirtualSensorModel(state_dim: int)

Bases: abc.ABC, torch.nn.Module

Virtual sensor base class for our differentiable Kalman filters.

Maps each observation input to a predicted state and uncertainty, in the style of BackpropKF. This is often necessary for complex observation spaces like images or point clouds, where it’s not possible to learn a standard state->observation measurement model.

state_dim

Dimensionality of our state.

Type:

int

abstract forward(self, *, observations: types.ObservationsTorch) → Tuple[types.StatesTorch, types.ScaleTrilTorch]

Predicts states and uncertainties from observation inputs.

Uncertainties should be lower-triangular Cholesky decompositions of covariance matrices.

Parameters:

observations (dict or torch.Tensor) – Measurement inputs. Should be either a dict of tensors or tensor of size (N, ...).

Returns:

Tuple[torch.Tensor, torch.Tensor] – Predicted states & uncertainties. States should have shape (N, state_dim), and uncertainties should be lower triangular with shape (N, state_dim, state_dim).