11 papers
Strategic Transformer for Resource-Constrained Multi-Object Navigation in Ultra-Large-Scale Environments
Daiki Iwata, Kanji Tanaka, Senta Hishida
Resource-constrained multi-object navigation in vast indoor environments () poses significant challenges for efficiency and strategic planning. To tackle this, w…
MOON: Multi-Objective Optimization-Driven Object-Goal Navigation Using a Variable-Horizon Set-Orienteering Planner
Daigo Nakajima, Kanji Tanaka, Daiki Iwata +1
This paper proposes MOON (Multi-Objective Optimization-driven Object-goal Navigation), a novel framework designed for efficient navigation in large-scale, complex indoor environmen…
LGR: LLM-Guided Ranking of Frontiers for Object Goal Navigation
Mitsuaki Uno, Kanji Tanaka, Daiki Iwata +3
Object Goal Navigation (OGN) is a fundamental task for robots and AI, with key applications such as mobile robot image databases (MRID). In particular, mapless OGN is essential in…
Dynamic-Dark SLAM: RGB-Thermal Cooperative Robot Vision Strategy for Multi-Person Tracking in Both Well-Lit and Low-Light Scenes
Tatsuro Sakai, Kanji Tanaka, Yuki Minase +3
In robot vision, thermal cameras hold great potential for recognizing humans even in complete darkness. However, their application to multi-person tracking (MPT) has been limited d…
Multi-Robot Data-Free Continual Communicative Learning (CCL) from Black-Box Visual Place Recognition Models
Kenta Tsukahara, Kanji Tanaka, Daiki Iwata +1
In emerging multi-robot societies, heterogeneous agents must continually extract and integrate local knowledge from one another through communication, even when their internal mode…
LMD-PGN: Cross-Modal Knowledge Distillation from First-Person-View Images to Third-Person-View BEV Maps for Universal Point Goal Navigation
Riku Uemura, Kanji Tanaka, Kenta Tsukahara +1
Point goal navigation (PGN) is a mapless navigation approach that trains robots to visually navigate to goal points without relying on pre-built maps. Despite significant progress…
ON as ALC: Active Loop Closing Object Goal Navigation
Daiki Iwata, Kanji Tanaka, Shoya Miyazaki +1
In simultaneous localization and mapping, active loop closing (ALC) is an active vision problem that aims to visually guide a robot to maximize the chances of revisiting previously…
CON: Continual Object Navigation via Data-Free Inter-Agent Knowledge Transfer in Unseen and Unfamiliar Places
Kouki Terashima, Daiki Iwata, Kanji Tanaka
This work explores the potential of brief inter-agent knowledge transfer (KT) to enhance the robotic object goal navigation (ON) in unseen and unfamiliar environments. Drawing on t…
Training Self-localization Models for Unseen Unfamiliar Places via Teacher-to-Student Data-Free Knowledge Transfer
Kenta Tsukahara, Kanji Tanaka, Daiki Iwata
A typical assumption in state-of-the-art self-localization models is that an annotated training dataset is available in the target workspace. However, this does not always hold whe…
Active Semantic Localization with Graph Neural Embedding
Mitsuki Yoshida, Kanji Tanaka, Ryogo Yamamoto +1
Semantic localization, i.e., robot self-localization with semantic image modality, is critical in recently emerging embodied AI applications (e.g., point-goal navigation, object-go…
Open-World Distributed Robot Self-Localization with Transferable Visual Vocabulary and Both Absolute and Relative Features
Mitsuki Yoshida, Ryogo Yamamoto, Daiki Iwata +1
Visual robot self-localization is a fundamental problem in visual robot navigation and has been studied across various problem settings, including monocular and sequential localiza…