Maximum Correntropy Criterion Kalman Filter For Indoor Quadrotor Navigation Under Intermittent Measurements
Date Issued
June 2023
Abstract
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.
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.
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