Onboard Real-Time Multi-Sensor Pose Estimation for Indoor Quadrotor Navigation with Intermittent Communication

TL;DR

This paper presents a real-time onboard multi-sensor fusion framework for indoor quadrotor navigation that effectively manages intermittent sensor data using a Kalman filter, demonstrated through hardware-in-the-loop simulations.

Contribution

The paper introduces a novel multisensor fusion approach with a Kalman filter tailored for handling intermittent sensor measurements in indoor quadrotor navigation.

Findings

Achieves low positioning errors in simulations

Handles intermittent sensor data effectively

Demonstrates real-time performance on AI-enabled edge GPU

Abstract

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.