  Knowing the position of the robot in the world is crucial for navigation.
Nowadays, Bayesian filters, such as Kalman and particle-based, are standard
approaches in mobile robotics. Recently, end-to-end learning has allowed for
scaling-up to high-dimensional inputs and improved generalization. However,
there are still limitations to providing reliable laser navigation. Here we
show a proof-of-concept of the predictive processing-inspired approach to
perception applied for localization and navigation using laser sensors, without
the need for odometry. We learn the generative model of the laser through
self-supervised learning and perform both online state-estimation and
navigation through stochastic gradient descent on the variational free-energy
bound. We evaluated the algorithm on a mobile robot (TIAGo Base) with a laser
sensor (SICK) in Gazebo. Results showed improved state-estimation performance
when comparing to a state-of-the-art particle filter in the absence of
odometry. Furthermore, conversely to standard Bayesian estimation approaches
our method also enables the robot to navigate when providing the desired goal
by inferring the actions that minimize the prediction error.
