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Poster
Modality-Agnostic Topology Aware Localization
Farhad Ghazvinian Zanjani · Ilia Karmanov · Hanno Ackermann · Daniel Dijkman · Simone Merlin · Max Welling · Fatih Porikli

Wed Dec 08 12:30 AM -- 02:00 AM (PST) @ Virtual
in Poster Session 3 »

This work presents a data-driven approach for the indoor localization of an observer on a 2D topological map of the environment. State-of-the-art techniques may yield accurate estimates only when they are tailor-made for a specific data modality like camera-based system that prevents their applicability to broader domains. Here, we establish a modality-agnostic framework (called OT-Isomap) and formulate the localization problem in the context of parametric manifold learning while leveraging optimal transportation. This framework allows jointly learning a low-dimensional embedding as well as correspondences with a topological map. We examine the generalizability of the proposed algorithm by applying it to data from diverse modalities such as image sequences and radio frequency signals. The experimental results demonstrate decimeter-level accuracy for localization using different sensory inputs.

Keywords: Optimal Transport

Author Information

Farhad Ghazvinian Zanjani (Qualcomm AI Research)
Ilia Karmanov (QualComm)
Hanno Ackermann (Leibniz University Hannover)
Daniel Dijkman (University of Amsterdam)
Simone Merlin (Qualcomm)
Max Welling (University of Amsterdam / Qualcomm AI Research)
Fatih Porikli (MERL)

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