A Multi-Sensor Fusion MAV State Estimation from Long-Range Stereo, IMU, GPS and Barometric SensorsReport as inadecuate


A Multi-Sensor Fusion MAV State Estimation from Long-Range Stereo, IMU, GPS and Barometric Sensors


A Multi-Sensor Fusion MAV State Estimation from Long-Range Stereo, IMU, GPS and Barometric Sensors - Download this document for free, or read online. Document in PDF available to download.

1

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

Abstract 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. View Full-Text

Keywords: multi-sensor fusion; GPS-denied state estimation; long-range stereo visual odometry; absolute and relative state measurements; stochastic cloning EKF multi-sensor fusion; GPS-denied state estimation; long-range stereo visual odometry; absolute and relative state measurements; stochastic cloning EKF





Author: Yu Song 1,2,* , Stephen Nuske 1 and Sebastian Scherer 1

Source: http://mdpi.com/



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