A Multi-Sensor Fusion MAV State Estimation from Long-Range Stereo, IMU, GPS and Barometric Sensors
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Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
2
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Academic Editors: Gabriel Oliver-Codina, Nuno Gracias and Antonio M. López
Sensors 2017, 17(1), 11; https://doi.org/10.3390/s17010011
Received: 1 October 2016
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Revised: 10 December 2016
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Accepted: 16 December 2016
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Published: 22 December 2016
(This article belongs to the Special Issue Vision-Based Sensors in Field Robotics)
State estimation is the most critical capability for MAV (Micro-Aerial Vehicle) localization, autonomous obstacle avoidance, robust flight control and 3D environmental mapping. There are three main challenges for MAV state estimation: (1) it can deal with aggressive 6 DOF (Degree Of Freedom) motion; (2) it should be robust to intermittent GPS (Global Positioning System) (even GPS-denied) situations; (3) it should work well both for low- and high-altitude flight. In this paper, we present a state estimation technique by fusing long-range stereo visual odometry, GPS, barometric and IMU (Inertial Measurement Unit) measurements. The new estimation system has two main parts, a stochastic cloning EKF (Extended Kalman Filter) estimator that loosely fuses both absolute state measurements (GPS, barometer) and the relative state measurements (IMU, visual odometry), and is derived and discussed in detail. A long-range stereo visual odometry is proposed for high-altitude MAV odometry calculation by using both multi-view stereo triangulation and a multi-view stereo inverse depth filter. The odometry takes the EKF information (IMU integral) for robust camera pose tracking and image feature matching, and the stereo odometry output serves as the relative measurements for the update of the state estimation. Experimental results on a benchmark dataset and our real flight dataset show the effectiveness of the proposed state estimation system, especially for the aggressive, intermittent GPS and high-altitude MAV flight.
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Keywords:
multi-sensor fusion; GPS-denied state estimation; long-range stereo visual odometry; absolute and relative state measurements; stochastic cloning EKF
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Externally hosted supplementary file 1
Link: https://www.youtube.com/watch?v=LYszVoEboWY
Description: Experimental video for EKF fusing state estimation, This is another experimental video for the proposed approach, not the same with the uploaded one.
MDPI and ACS Style
Song, Y.; Nuske, S.; Scherer, S. A Multi-Sensor Fusion MAV State Estimation from Long-Range Stereo, IMU, GPS and Barometric Sensors. Sensors 2017, 17, 11. https://doi.org/10.3390/s17010011
AMA Style
Song Y, Nuske S, Scherer S. A Multi-Sensor Fusion MAV State Estimation from Long-Range Stereo, IMU, GPS and Barometric Sensors. Sensors. 2017; 17(1):11. https://doi.org/10.3390/s17010011
Chicago/Turabian StyleSong, Yu, Stephen Nuske, and Sebastian Scherer. 2017. "A Multi-Sensor Fusion MAV State Estimation from Long-Range Stereo, IMU, GPS and Barometric Sensors" Sensors 17, no. 1: 11. https://doi.org/10.3390/s17010011
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