Scholarlink

<p>The demand for autonomous UAV navigation in complex environments is growing rapidly. However, most existing studies focus on simulations, lacking real-world validation under challenging conditions. This study systematically evaluates a vision-based navigation framework combining VINS-Mono for localization and Ego-Planner for path planning in GPS-denied, low-light, high-obstacle-density environments. Unlike traditional approaches that struggle with UAV high-frequency motion and dynamic constraints, VINS-Mono provides accurate high-frequency positioning via visual-inertial fusion. At the same time, Ego-Planner enables smooth, dynamically feasible real-time trajectory generation. Real-world experiments on three randomly generated maps (minimum obstacle spacing: 0.8&#xa0;m (twice UAV wheelbase); obstacle density: 0.72 obstacles/m<sup>2</sup>) achieved a minimum 70% success rate, with 87% of trajectories smoother than A* in simulations. Results demonstrate the framework’s robustness, though challenges such as field-of-view constraints and sensor-induced localization errors affecting replanning remain. This study presents a real-world evaluation of vision-based UAV navigation in constrained environments, offering insights into its feasibility and limitations. The findings contribute to improving trajectory optimization and UAV autonomy in constrained GPS-denied scenarios.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Vision-based autonomous navigation for quadrotor UAVs in unknown and constrained environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月23日

查看全文
  • Zhanhao Hu,
  • Muhammad Zohaib Butt,
  • Nazri Nasir,
  • Mohd Badrul Salleh

摘要

The demand for autonomous UAV navigation in complex environments is growing rapidly. However, most existing studies focus on simulations, lacking real-world validation under challenging conditions. This study systematically evaluates a vision-based navigation framework combining VINS-Mono for localization and Ego-Planner for path planning in GPS-denied, low-light, high-obstacle-density environments. Unlike traditional approaches that struggle with UAV high-frequency motion and dynamic constraints, VINS-Mono provides accurate high-frequency positioning via visual-inertial fusion. At the same time, Ego-Planner enables smooth, dynamically feasible real-time trajectory generation. Real-world experiments on three randomly generated maps (minimum obstacle spacing: 0.8 m (twice UAV wheelbase); obstacle density: 0.72 obstacles/m2) achieved a minimum 70% success rate, with 87% of trajectories smoother than A* in simulations. Results demonstrate the framework’s robustness, though challenges such as field-of-view constraints and sensor-induced localization errors affecting replanning remain. This study presents a real-world evaluation of vision-based UAV navigation in constrained environments, offering insights into its feasibility and limitations. The findings contribute to improving trajectory optimization and UAV autonomy in constrained GPS-denied scenarios.