  Generative state estimators based on probabilistic filters and smoothers are
one of the most popular classes of state estimators for robots and autonomous
vehicles. However, generative models have limited capacity to handle rich
sensory observations, such as camera images, since they must model the entire
distribution over sensor readings. Discriminative models do not suffer from
this limitation, but are typically more complex to train as latent variable
models for state estimation. We present an alternative approach where the
parameters of the latent state distribution are directly optimized as a
deterministic computation graph, resulting in a simple and effective gradient
descent algorithm for training discriminative state estimators. We show that
this procedure can be used to train state estimators that use complex input,
such as raw camera images, which must be processed using expressive nonlinear
function approximators such as convolutional neural networks. Our model can be
viewed as a type of recurrent neural network, and the connection to
probabilistic filtering allows us to design a network architecture that is
particularly well suited for state estimation. We evaluate our approach on
synthetic tracking task with raw image inputs and on the visual odometry task
in the KITTI dataset. The results show significant improvement over both
standard generative approaches and regular recurrent neural networks.
