Robot Localization and Navigation through Predictive Processing using LiDAR

Abstract

Knowing the position of the robot in the world is crucial for navigation. Nowadays, Bayesian filters, such as Kalman and particlebased, 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.

Active Inference in Robotics and Artificial Agents: Survey and Challenges

“…The dependency structure entailed by the planning over time for stochastic variables is as follows, (14) This factorization of the joint distribution over observations, hidden states, and action trajectories will also mirror the structure of our active inference agent's generative model.…”

“…VFE optimization has been successfully applied for adaptive robot estimation and learning in applications, such as body estimation [12], drones state tracking [9] and navigation [39], [14].…”