Robot Localization and Navigation through Predictive Processing using LiDAR
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2021-07-07
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en
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Abstract
Knowing the position of the robot in the world is crucial
for navigation. Nowadays, Bayesian lters, 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 lter 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.
Keywords: Predictive Processing · Robot localization · Robot navigation
· Laser sensor · LiDAR.
Note This Bachelor Thesis has been written in paper format and has
been submitted to the 2021 International Workshop on Active Inference
(IWAI) in conjuction with ECML/PKDD 2021.
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Faculteit der Sociale Wetenschappen