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Article

ESPEE: Event-Based Sensor Pose Estimation Using an Extended Kalman Filter

1
Temasek Laboratories, National University of Singapore, Singapore 117411, Singapore
2
Institute for Infocomm Research, A*STAR, Singapore 138632, Singapore
3
Open Source Robotics Corporation, Singapore 138633, Singapore
*
Author to whom correspondence should be addressed.
Academic Editors: Frantisek Duchon, Peter Hubinsky and Andrej Babinec
Sensors 2021, 21(23), 7840; https://doi.org/10.3390/s21237840
Received: 15 October 2021 / Revised: 18 November 2021 / Accepted: 20 November 2021 / Published: 25 November 2021
(This article belongs to the Collection Sensors and Data Processing in Robotics)
Event-based vision sensors show great promise for use in embedded applications requiring low-latency passive sensing at a low computational cost. In this paper, we present an event-based algorithm that relies on an Extended Kalman Filter for 6-Degree of Freedom sensor pose estimation. The algorithm updates the sensor pose event-by-event with low latency (worst case of less than 2 μs on an FPGA). Using a single handheld sensor, we test the algorithm on multiple recordings, ranging from a high contrast printed planar scene to a more natural scene consisting of objects viewed from above. The pose is accurately estimated under rapid motions, up to 2.7 m/s. Thereafter, an extension to multiple sensors is described and tested, highlighting the improved performance of such a setup, as well as the integration with an off-the-shelf mapping algorithm to allow point cloud updates with a 3D scene and enhance the potential applications of this visual odometry solution. View Full-Text
Keywords: event-based sensor; visual odometry; extended Kalman filter; computer vision; structureless measurement model event-based sensor; visual odometry; extended Kalman filter; computer vision; structureless measurement model
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MDPI and ACS Style

Colonnier, F.; Della Vedova, L.; Orchard, G. ESPEE: Event-Based Sensor Pose Estimation Using an Extended Kalman Filter. Sensors 2021, 21, 7840. https://doi.org/10.3390/s21237840

AMA Style

Colonnier F, Della Vedova L, Orchard G. ESPEE: Event-Based Sensor Pose Estimation Using an Extended Kalman Filter. Sensors. 2021; 21(23):7840. https://doi.org/10.3390/s21237840

Chicago/Turabian Style

Colonnier, Fabien, Luca Della Vedova, and Garrick Orchard. 2021. "ESPEE: Event-Based Sensor Pose Estimation Using an Extended Kalman Filter" Sensors 21, no. 23: 7840. https://doi.org/10.3390/s21237840

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