2024/11/04 by Thanh Nguyen Canh, Canh, Thanh Nguyen, Xiem HoangVan +4
Computer Science · Engineering · #FOS: Computer and information sciences #Robotic Path Planning Algorithms #Robotics (cs.RO) #Robotics and Automated Systems #Robotics and Sensor-Based Localization
paper · pdf · doi:10.48550/arxiv.2411.01816
openalex publication_date 2024/11/04 · openalex created_date 2024/11/14 · openalex updated_date 2026/07/28
Localization is one of the most crucial tasks for Unmanned Aerial Vehicle systems (UAVs) directly impacting overall performance, which can be achieved with various sensors and applied to numerous tasks related to search and rescue operations, object tracking, construction, etc. However, due to the negative effects of challenging environments, UAVs may lose signals for localization. In this paper, we present an effective path-planning system leveraging semantic segmentation information to navigate around texture-less and problematic areas like lakes, oceans, and high-rise buildings using a monocular camera. We introduce a real-time semantic segmentation architecture and a novel keyframe decision pipeline to optimize image inputs based on pixel distribution, reducing processing time. A hierarchical planner based on the Dynamic Window Approach (DWA) algorithm, integrated with a cost map, is designed to facilitate efficient path planning. The system is implemented in a photo-realistic simulation environment using Unity, aligning with segmentation model parameters. Comprehensive qualitative and quantitative evaluations validate the effectiveness of our approach, showing significant improvements in the reliability and efficiency of UAV localization in challenging environments.