6 papers
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…
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…
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…
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…
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…
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…