A Dual-Stream Transformer Architecture for Illumination-Invariant TIR-LiDAR Person Tracking
Robust person tracking is a critical capability for autonomous mobile robots operating in diverse and unpredictable environments. While RGB-D tracking has shown high precision, its performance severely degrades under challenging illumination conditions, such as total darkness or intense backlighting. To achieve all-weather robustness, this paper proposes a novel Thermal-Infrared and Depth (TIR-D) tracking architecture that leverages the standard sensor suite of SLAM-capable robots, namely LiDAR and TIR cameras. A major challenge in TIR-D tracking is the scarcity of annotated multi-modal datasets. To address this, we introduce a sequential knowledge transfer strategy that evolves structura...
| Authors | Yuki Minase, Kanji Tanaka |
|---|---|
| Primary category | cs.RO |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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