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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 drivenby unrealistic benchmarks not representative of real-world AR scenarios. Thesebenchmarks 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 mostlyinsufficient to satisfy AR requirements. To close this gap, we introduce LaMAR,a new benchmark with a comprehensive capture and GT pipeline that co-registersrealistic trajectories and sensor streams captured by heterogeneous AR devicesin large, unconstrained scenes. To establish an accurate GT, our pipelinerobustly aligns the trajectories against laser scans in a fully automatedmanner. As a result, we publish a benchmark dataset of diverse and large-scalescenes recorded with head-mounted and hand-held AR devices. We extend severalstate-of-the-art methods to take advantage of the AR-specific setup andevaluate them on our benchmark. The results offer new insights on currentresearch and reveal promising avenues for future work in the field oflocalization and mapping for AR.

LaMAR: Benchmarking Localization and Mapping for Augmented Reality