LFTag: A Scalable Visual Fiducial System with Low Spatial Frequency

TL;DR

LFTag is a new visual fiducial system that uses topological detection and data encoding to significantly increase dictionary size and detection range, outperforming existing markers like AprilTag and TopoTag.

Contribution

LFTag introduces a scalable fiducial system based on topological detection and data encoding, enabling larger dictionaries and longer detection ranges with robust false positive rejection.

Findings

LFTag 3x3 has 546 times larger dictionary than AprilTag 25h9.

LFTag 4x4 has 126,000 times larger dictionary than AprilTag 41h12.

LFTag achieves longer detection range than existing markers.

Abstract

Visual fiducial systems are a key component of many robotics and AR/VR applications for 6-DOF monocular relative pose estimation and target identification. This paper presents LFTag, a visual fiducial system based on topological detection and relative position data encoding which optimizes data density within spatial frequency constraints. The marker is constructed to resolve rotational ambiguity, which combined with the robust geometric and topological false positive rejection, allows all marker bits to be used for data. When compared to existing state-of-the-art square binary markers (AprilTag) and topological markers (TopoTag) in simulation, the proposed fiducial system (LFTag) offers significant advances in dictionary size and range. LFTag 3x3 achieves 546 times the dictionary size of AprilTag 25h9 and LFTag 4x4 achieves 126 thousand times the dictionary size of AprilTag 41h12 while simultaneously achieving longer detection range. LFTag 3x3 also achieves more than twice the detection range of TopoTag 4x4 at the same dictionary size.