Learning representations for simultaneous localisation and mapping
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2022-09-01
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en
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Abstract
Building a cognitive map from a continuous stream of visual inputs is
a non-trivial undertaking. While humans perform this task effortlessly,
current robotics systems often fail to do so. Possessing a rich internal
representation of the spatial layout is important for downstream tasks
such as navigation, scene understanding, or exploration. We start by
highlighting connections between different fields dealing with navigation,
such as simultaneous localization and mapping, animal foraging, and
world model learning. To determine which architecture is best suited
to build spatial representations, we compared eight different network
types trained in a self-supervised fashion on datasets generated using
3D maze environments of varying size and complexity. The compared
networks consist of both feedforward and recurrent models which performaction
sequence prediction and coordinate prediction tasks.We find
that models that integrate the largest amount of previous information
perform best. Local single-state information is insufficient to distinguish
identical-looking states and to uniquely determine the global position.
Our results imply that model-based navigation agents profit from integrating
trajectory information and that agents should thus be endowed
with mechanisms to do so.
Keywords Spatial Memory · Scene Representation · Representation
Learning · Self-supervised Learning · SLAM
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Faculteit der Sociale Wetenschappen