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Dong LT. Tran

Publications and source records attributed to Dong LT. Tran.

2 recordsLinked to original sources
arXiv2026

An Indoor Navigation System for the Visually Impaired based on UWB Positioning and D* Lite Path Planning Algorithm

This paper proposes an indoor navigation system for the visually impaired, leveraging Ultra-Wideband (UWB) positioning technology and the D*Lite 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.

cs.RO↗
arXiv2026

Cross-Session 3D LiDAR and Camera Fusion for Robust Localization of Unmanned Aerial Vehicles in GPS-Denied Environments

Accurate localization of unmanned aerial vehicles (UAVs) is essential for applications such as structural health monitoring, especially in environments where Global Positioning System (GPS) signals are denied or unreliable, like indoor spaces, tunnels, urban canyons, or areas beneath large structures. To address this challenge, we propose Cross-Fusion, a novel method for real-time UAV localization that integrates data from a 3D Light Detection and Ranging (LiDAR) and a monocular camera. A key contribution is its cross-session fusion strategy, which integrates visual and geometric information collected from multiple agents during routine baseline surveys to improve localization consistency and map completeness. The system employs LiDAR-based odometry for motion tracking and image-based feature matching via a single red-green-blue (RGB) camera to correct drift and improve accuracy. Unlike visual-inertial systems, Cross-Fusion maintains a simple sensor setup and avoids the complexity of stereo or global shutter configurations. Experimental results demonstrate that Cross-Fusion achieves localization accuracy comparable to GPS-based methods and performs reliably in challenging feature-sparse environments.

cs.RO↗