A Framework for Bearing-Only Sparse Semantic Self-Localization for Visually Impaired People

Uygur, Irem; Miyagusuku, Renato; Pathak, Sarthak; Moro, Alessandro; Yamashita, Atsushi; Asama, Hajime

doi:10.1109/sii.2019.8700370

Cited by 3 publications

(1 citation statement)

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“…We conducted different experiments to evaluate our sensor model both in a simulation and the real world. The experiments in the Unity simulation are detailed in our previous work [34]. In this section, we will focus on our experiments in the real world.…”

Section: Experimental Settingmentioning

confidence: 99%

Robust and Efficient Indoor Localization Using Sparse Semantic Information from a Spherical Camera

Uygur

Miyagusuku

Pathak

et al. 2020

Sensors

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Self-localization enables a system to navigate and interact with its environment. In this study, we propose a novel sparse semantic self-localization approach for robust and efficient indoor localization. “Sparse semantic” refers to the detection of sparsely distributed objects such as doors and windows. We use sparse semantic information to self-localize on a human-readable 2D annotated map in the sensor model. Thus, compared to previous works using point clouds or other dense and large data structures, our work uses a small amount of sparse semantic information, which efficiently reduces uncertainty in real-time localization. Unlike complex 3D constructions, the annotated map required by our method can be easily prepared by marking the approximate centers of the annotated objects on a 2D map. Our approach is robust to the partial obstruction of views and geometrical errors on the map. The localization is performed using low-cost lightweight sensors, an inertial measurement unit and a spherical camera. We conducted experiments to show the feasibility and robustness of our approach.

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Section: Experimental Settingmentioning

confidence: 99%