Dynamic-Dark SLAM: RGB-Thermal Cooperative Robot Vision Strategy for Multi-Person Tracking in Both Well-Lit and Low-Light Scenes
Journal:
arXiv
Published Date:
Mar 17, 2025
Abstract
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 due to data scarcity and the inherent difficulty of
distinguishing individuals. In this study, we propose a cooperative MPT system
that utilizes co-located RGB and thermal cameras, where pseudo-annotations
(bounding boxes and person IDs) are used to train both RGB and thermal
trackers. Evaluation experiments demonstrate that the thermal tracker performs
robustly in both bright and dark environments. Moreover, the results suggest
that a tracker-switching strategy -- guided by a binary brightness classifier
-- is more effective for information integration than a tracker-fusion
approach. As an application example, we present an image change pattern
recognition (ICPR) method, the ``human-as-landmark,'' which combines two key
properties: the thermal recognizability of humans in dark environments and the
rich landmark characteristics -- appearance, geometry, and semantics -- of
static objects (occluders). Whereas conventional SLAM focuses on mapping static
landmarks in well-lit environments, the present study takes a first step toward
a new Human-Only SLAM paradigm, ``DD-SLAM,'' which aims to map even dynamic
landmarks in complete darkness.
