Please use this identifier to cite or link to this item: https://doi.org/10.3390/s21237840
Title: Espee: Event-based sensor pose estimation using an extended kalman filter
Authors: Colonnier, Fabien 
Vedova, Luca Della
Orchard, Garrick 
Keywords: Computer vision
Event-based sensor
Extended Kalman filter
Structureless measurement model
Visual odometry
Issue Date: 25-Nov-2021
Publisher: MDPI
Citation: Colonnier, Fabien, Vedova, Luca Della, Orchard, Garrick (2021-11-25). Espee: Event-based sensor pose estimation using an extended kalman filter. Sensors 21 (23) : 7840. ScholarBank@NUS Repository. https://doi.org/10.3390/s21237840
Rights: Attribution 4.0 International
Abstract: 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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Source Title: Sensors
URI: https://scholarbank.nus.edu.sg/handle/10635/233013
ISSN: 1424-8220
DOI: 10.3390/s21237840
Rights: Attribution 4.0 International
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