2025/03/05 by Phuoc Nguyen, Nguyen, Phuoc, Francesco Verdoja +3 · 2 citations
Computer Science · Engineering · #FOS: Computer and information sciences #Graph Theory and Algorithms #Multimodal Machine Learning Applications #Robotics (cs.RO) #Robotics and Sensor-Based Localization #cs.RO
paper · pdf · doi:10.48550/arxiv.2503.03412
published as 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hangzhou, China, 2025, pp. 2209-2216 · Accepted to IROS 2025
openalex publication_date 2025/03/05 · arxiv created 2025/08/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28 · arxiv updated 2026/07/31
Modern-day autonomous robots need high-level map representations to perform sophisticated tasks. Recently, 3D scene graphs (3DSGs) have emerged as a promising alternative to traditional grid maps, blending efficient memory use and rich feature representation. However, most efforts to apply them have been limited to static worlds. This work introduces REACT, a framework that efficiently performs real-time attribute clustering and transfer to relocalize object nodes in a 3DSG. REACT employs a novel method for comparing object instances using an embedding model trained on triplet loss, facilitating instance clustering and matching. Experimental results demonstrate that REACT is able to relocalize objects while maintaining computational efficiency. The REACT framework's source code will be available as an open-source project, promoting further advancements in reusable and updatable 3DSGs.