Robust Global Tracking Using a Seamless Structured Pattern of Dots

Abstract

Abstract. In this paper, we present a novel optical tracking approach to accurately estimate the pose of a camera in large scene augmented reality (AR). Traditionally, larger scenes are provided with multiple markers with their own identifier and coordinate system. However, when any part of a single marker is occluded, the marker cannot be identified. Our system uses a seamless structure of dots where the world position of each dot is represented by its spatial relation to neighboring dots. By using only the dots as features, our marker can be robustly identified. We use projective invariants to estimate the global position of the features and exploit temporal coherence using optical flow. With this design, our system is more robust against occlusions. It can also give the user more freedom of movement allowing them to explore objects up close and from a distance.

Tracking and Co-Location of Global Point Clouds for Large-Area Indoor Environments

“…The aforementioned tracking methods rely on natural image features to identify landmarks in the environment to retrieve a known calibrated position within the tracking area. When natural images features are not a strict requirement for the application at hand, a set of artificial features can be introduced consisting of bar codes, QR codes, ArUco (Garrido-Jurado et al, 2014) or alternatively designed custom markers (Jorissen et al, 2014). Maesen et al (2013) and Podkosova et al (2016) have laid the groundwork for this approach with their fundamental research on large-area tracking, but evaluation has only been performed in very constrained environments.…”

Quickest sequence phase detection

“…where Int(p, 3µ) is given in (10). This bound can sometimes be better than the second MRRW bound [18], which is the best known asymptotic upper bound for binary channel codes.…”

Real-time high-accuracy 2D localization with structured patterns

“…[2] comes close to meeting these criteria, but the nature of this method makes real-time localization and device miniaturization difficult due to the capacitive sensor array requirement. [3,4] describe structured optical patterns designed for 3D localization in open spaces; however, miniaturizing these patterns for our application would either significantly limit the size of the localization space or increase the minimum image resolution requirement and processing power need.…”