We present a multisensor fusion framework for the onboard real-time navigation of a quadrotor in an indoor environment. The framework integrates sensor readings from an Inertial Measurement Unit (IMU), a camera-based object detection algorithm, and an Ultra-WideBand (UWB) localisation system. Often the sensor readings are not always readily available, leading to inaccurate pose estimation and hence poor navigation performance. To effectively handle and fuse sensor readings, and accurately estimate the pose of the quadrotor for tracking a predefined trajectory, we design a Maximum Correntropy Criterion Kalman Filter (MCC-KF) that can manage intermittent observations. The MCC-KF is designed to improve the performance of the estimation process when is done with a Kalman Filter (KF), since KFs are likely to degrade dramatically in practical scenarios in which noise is non-Gaussian (especially when the noise is heavy-tailed). To evaluate the performance of the MCC-KF, we compare it with a previously designed Kalman filter by the authors. Through this comparison, we aim to demonstrate the effectiveness of the MCC-KF in handling indoor navigation missions. The simulation results show that our presented framework offers low positioning errors, while effectively handling intermittent sensor measurements.

翻译：我们提出了一种用于室内环境下四旋翼机载实时导航的多传感器融合框架。该框架集成了惯性测量单元（IMU）、基于相机的目标检测算法以及超宽带（UWB）定位系统的传感器读数。由于传感器读数并非始终可用，常常导致位姿估计不准确，进而造成导航性能下降。为了有效处理并融合传感器读数、精确估计四旋翼位姿以跟踪预设轨迹，我们设计了一种能够处理间歇性观测的最大相关熵准则卡尔曼滤波器（MCC-KF）。由于卡尔曼滤波器（KF）在实际场景中噪声非高斯（尤其是重尾噪声时）易出现性能急剧退化，MCC-KF旨在提升此类估计过程的性能。为评估MCC-KF的性能，我们将其与作者先前设计的卡尔曼滤波器进行对比。通过该对比，我们旨在证明MCC-KF在处理室内导航任务中的有效性。仿真结果表明，所提出的框架在有效处理间歇性传感器测量的同时，可实现低定位误差。