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

Content Provider	IEEE Xplore Digital Library
Author	Lynen, S. Achtelik, M.W. Weiss, S. Chli, M. Siegwart, R.
Copyright Year	2013
Description	Author affiliation: Autonomous Syst. Lab., ETH Zurich, Zurich, Switzerland (Lynen, S.; Achtelik, M.W.; Chli, M.; Siegwart, R.) \|\| Comput. Vision Group, Nasa JPL, Pasadena, CA, USA (Weiss, S.)
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.
Sponsorship	IEEE Ind. Electron. Soc.
Starting Page	3923
Ending Page	3929
File Size	1198100
Page Count	7
File Format	PDF
ISSN	21530858
DOI	10.1109/IROS.2013.6696917
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Time measurement Simultaneous localization and mapping Global Positioning System Calibration Current measurement
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications Computer Vision and Pattern Recognition Software

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