Publication

High-frequency MAV state estimation using low-cost inertial and optical flow measurement units

Conference Article

Conference

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Edition

2015

Pages

1864-1871

Doc link

http://dx.doi.org/10.1109/IROS.2015.7353621

File

Download the digital copy of the doc pdf document

Abstract

This paper develops a new method for 3D, high rate vehicle state estimation, specially designed for free-flying Micro Aerial Vehicles (MAVs). We fuse observations from inertial and optical flow low-cost measurement units, and extend the current use of this optical sensors from hovering purposes to odometry estimation. Two Kalman filters, with its extended and error-state versions, are developed, and benchmarked alongside a large number of algorithm variations, using both simulations and real experiments with precise ground-truth. In contrast to state-of-the-art visual-inertial odometry methods, the proposed solution does not require image processing in the main CPU. Instead, the data correction is done taking advantage of the recently appeared optical flow sensors, which directly provide metric information about the MAV motion. We hence reduce the computational load of the main processor unit, and obtain an accurate estimation of the vehicle state at a high update rate.

Categories

aerospace robotics, mobile robots.

Author keywords

sensor fusion, filtering, state estimation

Scientific reference

A. Santamaria-Navarro, J. Solà and J. Andrade-Cetto. High-frequency MAV state estimation using low-cost inertial and optical flow measurement units, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2015, Hamburg, pp. 1864-1871.