Akira Taniguchi — accepted AI-conference papers

2024

Object Instance Retrieval in Assistive Robotics: Leveraging Fine-Tuned SimSiam with Multi-View Images Based on 3D Semantic Map

Taichi Sakaguchi, Akira Taniguchi, Yoshinobu Hagiwara, Lotfi El Hafi, Shoichi Hasegawa, Tadahiro Taniguchi

IROS 2024poster

Robots that assist humans in their daily lives should be able to locate specific instances of objects in an environment that match a user’s desired objects. This task is known as instance-specific image goal navigation (InstanceImageNav), which requires a model that can distinguish different instanc…

Cited by 7 Source ↗code ↗Scholar ↗

2022

Multimodal Object Categorization with Reduced User Load through Human-Robot Interaction in Mixed Reality

Hitoshi Nakamura, Lotfi El Hafi, Akira Taniguchi, Yoshinobu Hagiwara, Tadahiro Taniguchi

IROS 2022poster

Enabling robots to learn from interactions with users is essential to perform service tasks. However, as a robot categorizes objects from multimodal information obtained by its sensors during interactive onsite teaching, the inferred names of unknown objects do not always match the human user's expe…

Cited by 3 Source ↗Scholar ↗

2020

SpCoMapGAN: Spatial Concept Formation-based Semantic Mapping with Generative Adversarial Networks

Yuki Katsumata, Akira Taniguchi, Lotfi El Hafi, Yoshinobu Hagiwara, Tadahiro Taniguchi

IROS 2020poster

In semantic mapping, which connects semantic information to an environment map, it is a challenging task for robots to deal with both local and global information of environments. In addition, it is important to estimate semantic information of unobserved areas from already acquired partial observat…

Cited by 18 Source ↗Scholar ↗

2017

Online spatial concept and lexical acquisition with simultaneous localization and mapping

Akira Taniguchi, Yoshinobu Hagiwara, Tadahiro Taniguchi, Tetsunari Inamura

IROS 2017poster

In this paper, we propose an online learning algorithm based on a Rao-Blackwellized particle filter for spatial concept acquisition and mapping. We have proposed a nonparametric Bayesian spatial concept acquisition model (SpCoA). We propose a novel method (SpCoSLAM) integrating SpCoA and FastSLAM in…

Cited by 64 Source ↗Scholar ↗