cs.RO · 2607.16614 Copy arXiv ID · Jul 18, 2026 Save An Indoor Navigation System for the Visually Impaired based on UWB Positioning and D* Lite Path Planning Algorithm Authors: Thanh C. Vo , Dong LT. Tran , Huy HM. Le , Duyen N Ha , Tuan Anh Pham , Hai Thanh Dang , Hoang T. Tran
Organizations: Viện Công nghệ Kỹ thuật Hàng không Vũ trụ, Đại học Duy Tân, Đà Nẵng
Abstract This paper proposes an indoor navigation system for the visually impaired, leveraging Ultra-Wideband (UWB) positioning technology and the DLite path planning algorithm. The system utilizes UWB sensors to provide precision localization in GPS-denied environments. The D Lite algorithm is integrated to optimize travel trajectories and ensure rapid route re-planning in the presence of dynamic obstacles. Experimental results demonstrate that the system operates reliably with low latency, providing safety and flexibility for users in complex indoor spaces.
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Multimodal AI, powered by Large Language Models (LLMs) and Vision-Language Models (VLMs), is transforming assistive technologies by enabling simultaneous processing of visual and textual data. This advancement holds significant promise for over 43 million visually impaired and neuro-divergent individuals worldwide who face persistent challenges in navigating indoor and outdoor environments due to limited spatial awareness and insufficient environmental cues. Existing navigation aids often lack comprehensive 3D scene understanding, relying on constrained route-based strategies that hinder user autonomy. In this paper, we introduce a novel end-to-end framework that integrates LLMs, VLMs and digital twin technologies to deliver a spatially cognitive navigation support for visually impaired and neuro-divergent users. Our system captures video input via standard mobile phone cameras, and employs SLAM3R to generate dense 3D point clouds from monocular RGB sequences in real-time. Our custom post-processing algorithm ensures accurate point cloud alignment across multiple viewpoints without requiring predefined reference points. This enhances the capabilities of SpatialLM to produce structured 3D representations, including architectural elements and oriented object bounding boxes. The enriched spatial data is then processed by a locally deployed LLM, which interprets 3D contexts to generate detailed scene descriptions and precise distance measurements between users and surrounding objects. We evaluated our approach across diverse video scenarios featuring various perspectives, looped walking views and captured in multiple environments. The evaluation results demonstrate consistent accuracy in 3D scene interpretation and object localisation, underscoring the potential of our system as a transformative assistive navigation solution that combines advanced visual perception with spatial reasoning