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ESPEE: Event-Based Sensor Pose Estimation Using an Extended Kalman Filter.
- Source :
- Sensors (14248220); Dec2021, Vol. 21 Issue 23, p7840, 1p
- Publication Year :
- 2021
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- KALMAN filtering
VISUAL odometry
ALGORITHMS
DETECTORS
IMAGE sensors
POINT cloud
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 21
- Issue :
- 23
- Database :
- Complementary Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 154080457
- Full Text :
- https://doi.org/10.3390/s21237840