Paper Info
Title
Real-Time Onboard Visual-Inertial State Estimation And Self-Calibration Of Mavs In Unknown Environments
Abstract
The combination of visual and inertial sensors has proved to be very popular in robot navigation and, in particular, Micro Aerial Vehicle (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.In this paper, we propose a navigation algorithm for MAVs equipped with a single camera and an Inertial Measurement Unit (IMU) which is able to run onboard and in real-time. The main focus here 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 on board 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 40Hz on an on board Atom computer 1.6 GHz.
Year
DOI
Venue
2012
10.1109/ICRA.2012.6225147
2012 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
Keywords
Field
DocType
robustness,inertial sensors,inertial measurement unit,pose estimation,inertial navigation,optical flow,mobile robots,navigation,computational complexity,optical sensor,estimation,real time,speed control
Inertial navigation system,Computer vision,Observability,Extended Kalman filter,Pose,Control engineering,Artificial intelligence,Inertial measurement unit,Engineering,Initialization,Optical flow,Mobile robot
Conference
Volume
Issue
ISSN
2012
1
1050-4729
Citations 
PageRank 
References 
98
3.80
11
Authors
5
Name
Order
Citations
PageRank
Stephan Weiss1102258.90
Markus Achtelik271032.78
Simon Lynen361726.48
Margarita Chli4128353.59
Roland Siegwart57640551.49