Dense 3D mapping with semantic understanding is essential for robotic perception in complex environments. Recent 3D Gaussian Splatting-based mapping approaches enable high-fidelity geometry and efficient open-vocabulary perception, but typically represent semantics as unstructured feature fields that limit object-centr...
B. Nguyen, Kien Nguyen, Clinton Fookes et al.· 0 citations
SGCA-Net is proposed, a spinning radar place recognition framework that combines rotation-robust feature extraction with Spatially Gated Correlation Aggregation (SGCA) and consistently outperforms SOTA methods across urban, campus, and open-road environments, while remaining robust to substantial heading variation.
Saimunur Rahman, Sagun Shrestha, Abdelwahed Khamis et al.· 0 citations
Results show that GFM-derived geometry can provide a persistent structural prior for VPR when visual appearance becomes unreliable, with the largest gains under reverse traversal and long-term appearance variation.
Walter Nedov, Saimunur Rahman, Kavindie Katuwandeniya et al.· 0 citations
This letter introduces a language-guided object removal that combines neural field resampling with multiview-consistent progressive inpainting, a direct NeRF weight editing method utilizing knowledge distillation, and the first benchmark (NEO-Dataset) for quantitatively evaluating NeRF scene editing methods suitable fo...
M. Zieliński, David Hall, Dominik Belter et al.· IEEE Robotics and Automation...· 0 citations
TOLiD is proposed, a self-supervised pretraining method for LiDAR representation learning that addresses the gap between dense ViT token representations and sparse 3D encoders by coupling a LiDAR backbone with a student Vision Transformer initialized from a frozen VFM teacher and applying supervision over compatible pa...
Sutharsan Mahendran, Darshana Priyasad, Kaushik Roy et al.· arXiv.org· 1 citation
GemNav is introduced, a visual robot navigation policy that adapts a frozen Multimodal Large Language Model (MLLM) for short-to-medium horizon waypoint navigation using Low-Rank Adaptation (LoRA) on the language tower alone, and results indicate that discrete-token adaptation of frozen MLLMs can provide a data-efficien...
Peter Bohm, Saimunur Rahman, Abdelwahed Khamis et al.· 0 citations
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