An efficient visual fiducial localisation system

Abstract

With use cases that range from external localisation of single robots or robotic swarms to self-localisation in markeraugmented environments and simplifying perception by tagging objects in a robot's surrounding, fiducial markers have a wide field of application in the robotic world. We propose a new family of circular markers which allow for both computationally efficient detection, tracking and identification and full 6D position estimation. At the core of the proposed approach lies the separation of the detection and identification steps, with the former using computationally efficient circular marker detection and the latter utilising an open-ended 'necklace encoding', allowing scalability to a large number of individual markers. While the proposed algorithm achieves similar accuracy to other state-of-the-art methods, its experimental evaluation in realistic conditions demonstrates that it can detect markers from larger distances while being up to two orders of magnitude faster than other stateof-the art fiducial marker detection methods. In addition, the entire system is available as an open-source package at https://github.com/LCAS/whycon.

Shaping the collision selectivity in a looming sensitive neuron model with parallel ON and OFF pathways and spike frequency adaptation

“…were also ID-specific patterns on the top of the Colias robot and all stationary obstacles that were tracked by a practical localisation system [68,69,70], to get the robots overall trajectories and calculate the success rates of collision detections.…”

L*a*b*Fruits: A Rapid and Robust Outdoor Fruit Detection System Combining Bio-Inspired Features with One-Stage Deep Learning Networks

“…The production site where the data was captured was at the University of Lincoln research farm at Riseholme campus. Two poly tunnels with table top strawberry rows were constructed, one row was tagged with visual markers [39] to indicate the points along the row where data should be collected, and the subsequent data collection process occurred singularly on this tagged row three times a day three times a week to capture various light intensities, weather conditions and plant growth stages. The species of strawberry was Amesti, captured at the flowering and fruiting stages of the plant.…”

A Hybrid Marker-Based Indoor Positioning System for Pedestrian Tracking in Subway Stations

“…A method of measuring the user's location was used by recognizing colors through a camera attached to the ceiling and converting the location into a location matrix. Meanwhile, Lightbody et al [23] proposed a new marker and introduced a positioning system that applied it. The new marker added encoded information based on the concept of binary necklaces to the existing WhyCon circular marker.…”