Validation of Vision-Based State Estimation for Localization of Agents and Swarm Formation

Fekrmandi, Hadi; Rutan-Bedard, Skye I.; Frye, Alexander; Hoover, Randy C.

doi:10.1007/978-3-030-43929-3_20

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…One of their most important applications is augmented reality [40,45,37] however they are also used in other fundamental computer vision problems such as camera calibration [2,14], 3D reconstruction [8,35,39], and SLAM [30,32]. Additionally, they are applied to unmanned aerial vehicles [36,3] medicine [39,40], and swarm robotics [44,11] amongst other applications. One of the first works to design a marker with binary squares for coding and a black edge for easy detection is Matrix [34].…”

Section: Binary Square Marker Detection In Regular Camerasmentioning

confidence: 99%

Detection of Binary Square Fiducial Markers Using an Event Camera

Sarmadi,

Muñoz-Salinas,

Olivares-Mendez

et al. 2020

Preprint

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Event cameras are a new type of image sensors that output changes in light intensity (events) instead of absolute intensity values. They have a very high temporal resolution and a high dynamic range. In this paper, we propose a method to detect and decode binary square markers using an event camera. We detect the edges of the markers by detecting line segments in an image created from events in the current packet. The line segments are combined to form marker candidates. The bit value of marker cells is decoded using the events on their borders. To the best of our knowledge, no other approach exists for detecting square binary markers directly from an event camera. Experimental results show that the performance of our proposal is much superior to the one from the RGB ArUco marker detector. Additionally, the proposed method can run on a single CPU core in real-time.

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