The utilization of Unmanned Aerial Vehicles (UAVs) like drones for indoor data gathering or sensing applications have gained popularity over the last decade. Indoor UAV navigation is a complex process, which involves several tasks such as mapping, localization, and path planning with obstacle avoidance. In this paper, a Particle Filter-based Indoor Navigation (PFIN) framework is proposed for the drone navigation process. In PFIN, Quadcopter Mapping Algorithm (QMA) is proposed using particle filter analysis to aid in mapping for indoor navigation. In addition, particle filter-based Optimized Localization Algorithm (OLA) and Adaptive Velocity Procedure (AVP) are proposed for the purpose of enhancing the precision in localization and to improve the velocity estimation for collision avoidance, respectively. Thus, the proposed PFIN framework is experimented using Software-In-The-Loop (SITL) tools such as Robot Operating System (ROS) and Gazebo for visualizing its behavior, and Crazyflie 2.0 drone assisted Hardware-In-The-Loop (HITL) simulation in verifying the correctness of the algorithms in a laboratory setup. The PFIN framework reduces position error on an average by 14% than the conventional Extended Kalman Filter (EKF) model. The SITL and HITL simulations demonstrate the efficiency of the algorithms through improved precision in UAV exploration.

中文翻译：

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PFIN：基于粒子过滤器的高效无人机室内导航框架


在过去的十年中，利用无人机（UAV）（例如无人机）进行室内数据收集或传感应用已变得越来越流行。室内无人机导航是一个复杂的过程，涉及建图、定位、避障路径规划等多项任务。本文为无人机导航过程提出了一种基于粒子滤波器的室内导航（PFIN）框架。在 PFIN 中，提出了四轴飞行器测绘算法 (QMA)，使用粒子滤波器分析来辅助室内导航测绘。此外，提出了基于粒子滤波器的优化定位算法（OLA）和自适应速度程序（AVP），分别用于提高定位精度和改进避免碰撞的速度估计。因此，所提出的 PFIN 框架使用软件在环 (SITL) 工具（例如机器人操作系统 (ROS) 和 Gazebo 来可视化其行为）以及 Crazyflie 2.0 无人机辅助硬件在环 (HITL) 进行实验模拟验证实验室设置中算法的正确性。 PFIN 框架比传统的扩展卡尔曼滤波器 (EKF) 模型平均减少了 14% 的位置误差。 SITL 和 HITL 模拟通过提高无人机探索精度来证明算法的效率。