Authors:
Peter Aerts
and
Eric Demeester
Affiliation:
ACRO Research Group, Department of Mechanical Engineering, University of Leuven, Diepenbeek and Belgium
Keyword(s):
Time Synchronisation, Particle Filter, Clock Parameter Estimation, Camera/IMU Synchronisation.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Mobile Robots and Autonomous Systems
;
Perception and Awareness
;
Robotics and Automation
;
Sensors Fusion
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
Clock synchronisation between sensors plays a key role in applications such as in autonomous robot navigation and mobile robot mapping. Such robots are often equipped with cameras for gathering visual information. In this work, we address the problem of synchronizing visual data collected from a low-cost 2D camera, with IMU (Inertial Measurement Unit) data. Both sensors are assumed to be attached to the same rigid body; hence, their motion is correlated. We present a motion based approach using a particle filter to estimate the clock parameters of the camera with the IMU clock as a reference. We apply the Lucas-Kanade optical flow method to calculate the movements of the camera in its horizontal plane corresponding to its recorded images. These movements are correlated to the motion registered by the IMU. This match allows a particle filter to determine the camera clock parameters in the IMU’s time frame and are used to calculate the timestamps of the images. We presume that only the
IMU sensor provides timestamp data generated from its internal clock. Our experiments show that given enough features are present within the images, this approach has the ability to provide the image timestamps within the IMU’s time frame.
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