论文标题
板上实时多传感器姿势估计室内四极管导航，并进行间歇性通信
Onboard Real-Time Multi-Sensor Pose Estimation for Indoor Quadrotor Navigation with Intermittent Communication
论文作者
论文摘要
我们通过集成了惯性测量单元（IMU）的传感器读数，基于摄像机的对象检测算法和Ultra-Wideband（UWB）本地化系统，提出了一个用于在室内环境中实时导航的多传感器融合框架。基于相机的对象检测算法和UWB本地化系统的传感器读数间歇性地到达，因为测量不容易获得。我们设计了一个Kalman滤波器，该滤波器可以管理间歇性观测值，以便处理和融合读数并估算四型姿势，以跟踪预定义轨迹。该系统是通过硬件（HIL）仿真技术实现的，其中四极管的动态模型是在开源3D机器人模拟器工具中模拟的，并且在启用人工智能（AI）的Edge Edge GPU上实现了整个导航系统。仿真结果表明，我们提出的框架提供了低位置和轨迹误差，同时处理间歇性传感器测量值。
We propose a multisensor fusion framework for onboard real-time navigation of a quadrotor in an indoor environment, by integrating sensor readings from an Inertial Measurement Unit (IMU), a camera-based object detection algorithm, and an Ultra-WideBand (UWB) localization system. The sensor readings from the camera-based object detection algorithm and the UWB localization system arrive intermittently, since the measurements are not readily available. We design a Kalman filter that manages intermittent observations in order to handle and fuse the readings and estimate the pose of the quadrotor for tracking a predefined trajectory. The system is implemented via a Hardware-in-the-loop (HIL) simulation technique, in which the dynamic model of the quadrotor is simulated in an open-source 3D robotics simulator tool, and the whole navigation system is implemented on Artificial Intelligence (AI) enabled edge GPU. The simulation results show that our proposed framework offers low positioning and trajectory errors, while handling intermittent sensor measurements.
