ICRA 2012 Paper Abstract

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Paper TuC01.4

Weiss, Stephan (ETH Zurich), Achtelik, Markus W. (ETH Zurich, Autonomous Systems Lab), Lynen, Simon (ETH Zurich), Chli, Margarita (ETH Zurich), Siegwart, Roland (ETH Zurich)

Real-Time Onboard Visual-Inertial State Estimation and Self-Calibration of MAVs in Unknown Environments

Scheduled for presentation during the Regular Session "Autonomy and Vision for UAVs" (TuC01), Tuesday, May 15, 2012, 15:15−15:30, Meeting Room 1 (Mini-sota)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on May 25, 2018

Keywords Aerial Robotics, Calibration and Identification, Visual Navigation

Abstract

The combination of visual and inertial sensors has proved to be very popular in MAV navigation due the flexibility in weight, power consumption and low cost it offers. At the same time, coping with the big latency between inertial and visual measurements and processing images in real-time impose great research challenges. Most modern MAV navigation systems avoid to explicitly tackle this by employing a ground station for off-board processing. We propose a navigation algorithm for MAVs equipped with a single camera and an IMU which is able to run onboard and in real-time. The main focus is on the proposed speed-estimation module which converts the camera into a metric body-speed sensor using IMU data within an EKF framework. We show how this module can be used for full self-calibration of the sensor suite in real-time. The module is then used both during initialization and as a fall-back solution at tracking failures of a keyframe-based VSLAM module. The latter is based on an existing high-performance algorithm, extended such that it achieves scalable 6DoF pose estimation at constant complexity. Fast onboard speed control is ensured by sole reliance on the optical flow of at least two features in two consecutive camera frames and the corresponding IMU readings. Our nonlinear observability analysis and our real experiments demonstrate that this approach can be used to control a MAV in speed, while we also show results of operation at 40 Hz on an onboard Atom computer 1.6 GHz.

 

 

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