A robust and modular multi-sensor fusion approach applied to MAV navigation

TLDR

Fusing multiple sensors improves robot navigation robustness and accuracy, yet calibration, outages, differing rates and delays make it difficult, so systems often omit available sensors—e.g., ignoring GPS when transitioning indoors to outdoors—leading to reduced robustness and accuracy in real deployments. This paper introduces the MultiSensor‑Fusion Extended Kalman Filter (MSF‑EKF), a generic framework that processes delayed, relative, and absolute measurements from any number of sensor types while enabling online self‑calibration. MSF‑EKF’s modular design handles sensor addition or loss during operation, employs state buffering with Iterated EKF updates for efficient re‑linearization, and is validated on an outdoor MAV equipped with GPS, vision, inertial, and pressure sensors.

Abstract

It has been long known that fusing information from multiple sensors for robot navigation results in increased robustness and accuracy. However, accurate calibration of the sensor ensemble prior to deployment in the field as well as coping with sensor outages, different measurement rates and delays, render multi-sensor fusion a challenge. As a result, most often, systems do not exploit all the sensor information available in exchange for simplicity. For example, on a mission requiring transition of the robot from indoors to outdoors, it is the norm to ignore the Global Positioning System (GPS) signals which become freely available once outdoors and instead, rely only on sensor feeds (e.g., vision and laser) continuously available throughout the mission. Naturally, this comes at the expense of robustness and accuracy in real deployment. This paper presents a generic framework, dubbed MultiSensor-Fusion Extended Kalman Filter (MSF-EKF), able to process delayed, relative and absolute measurements from a theoretically unlimited number of different sensors and sensor types, while allowing self-calibration of the sensor-suite online. The modularity of MSF-EKF allows seamless handling of additional/lost sensor signals during operation while employing a state buffering scheme augmented with Iterated EKF (IEKF) updates to allow for efficient re-linearization of the prediction to get near optimal linearization points for both absolute and relative state updates. We demonstrate our approach in outdoor navigation experiments using a Micro Aerial Vehicle (MAV) equipped with a GPS receiver as well as visual, inertial, and pressure sensors.