Colonnier, F., Della Vedova, L., & Orchard, G. (2021). ESPEE: Event-Based Sensor Pose Estimation Using an Extended Kalman Filter. Sensors, 21(23), 7840. doi:10.3390/s21237840
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.
Attribution 4.0 International (CC BY 4.0)
This research / project is supported by the National Research Foundation Singapore - Innovation and Enterprise 2020 plan (Advanced Manufacturing and Engi- neering domain).
Grant Reference no. : A1687b0033