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|Title:||Perception in the dark; development of a tof visual inertial odometry system||Authors:||Chen, SY
|Issue Date:||2020||Source:||Sensors, 1 Mar. 2020, v. 20, no. 5, 1263, p. 1-25||Abstract:||Visual inertial odometry (VIO) is the front-end of visual simultaneous localization and mapping (vSLAM) methods and has been actively studied in recent years. In this context, a time-of-flight (ToF) camera, with its high accuracy of depth measurement and strong resilience to ambient light of variable intensity, draws our interest. Thus, in this paper, we present a realtime visual inertial system based on a low cost ToF camera. The iterative closest point (ICP) methodology is adopted, incorporating salient point-selection criteria and a robustness-weighting function. In addition, an error-state Kalman filter is used and fused with inertial measurement unit (IMU) data. To test its capability, the ToF-VIO system is mounted on an unmanned aerial vehicle (UAV) platform and operated in a variable light environment. The estimated flight trajectory is compared with the ground truth data captured by a motion capture system. Real flight experiments are also conducted in a dark indoor environment, demonstrating good agreement with estimated performance. The current system is thus shown to be accurate and efficient for use in UAV applications in dark and Global Navigation Satellite System (GNSS)-denied environments.||Keywords:||VIO
Error-state Kalman Filter
|Publisher:||Molecular Diversity Preservation International (MDPI)||Journal:||Sensors||EISSN:||1424-8220||DOI:||10.3390/s20051263||Rights:||© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Chen, S.; Chang, C.-W.; Wen, C.-Y. Perception in the Dark; Development of a ToF Visual Inertial Odometry System. Sensors 2020, 20, 1263 is available at https://dx.doi.org/10.3390/s20051263
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Citations as of Jul 12, 2020
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