[2210.10770] LaMAR: Benchmarking Localization and Mapping for Augmented Reality

Localization and mapping is the foundational technology for augmented reality
(AR) that enables sharing and persistence of digital content in the real world.
While significant progress has been made, researchers are still mostly driven
by unrealistic benchmarks not representative of real-world AR scenarios. These
benchmarks are often based on small-scale datasets with low scene diversity,
captured from stationary cameras, and lack other sensor inputs like inertial,
radio, or depth data. Furthermore, their ground-truth (GT) accuracy is mostly
insufficient to satisfy AR requirements. To close this gap, we introduce LaMAR,
a new benchmark with a comprehensive capture and GT pipeline that co-registers
realistic trajectories and sensor streams captured by heterogeneous AR devices
in large, unconstrained scenes. To establish an accurate GT, our pipeline
robustly aligns the trajectories against laser scans in a fully automated
manner. As a result, we publish a benchmark dataset of diverse and large-scale
scenes recorded with head-mounted and hand-held AR devices. We extend several
state-of-the-art methods to take advantage of the AR-specific setup and
evaluate them on our benchmark. The results offer new insights on current
research and reveal promising avenues for future work in the field of
localization and mapping for AR.