LaMAR: Benchmarking Localization and Mapping for Augmented Reality
This addresses the need for realistic benchmarks in AR localization and mapping, which is foundational for sharing and persisting digital content, but it is incremental as it builds on existing methods with a new dataset.
The authors tackled the problem of unrealistic benchmarks for localization and mapping in augmented reality by introducing LaMAR, a new benchmark with large-scale, diverse scenes and accurate ground-truth, resulting in the publication of a dataset and evaluation of state-of-the-art methods that reveal new research insights.
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.