LaMAR: Benchmarking Localization and Mapping for Augmented Reality

Paul-Edouard Sarlin, Mihai Dusmanu, Johannes L. Schönberger, Pablo Speciale, Lukas Gruber, Viktor Larsson, O. Mikšík, M. Pollefeys
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引用次数: 23

Abstract

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.
LaMAR:增强现实的基准定位和映射
定位和映射是增强现实(AR)的基础技术,它使数字内容能够在现实世界中共享和持久。虽然已经取得了重大进展,但研究人员仍然主要受到不切实际的基准的驱动,而不是代表现实世界的AR场景。这些基准通常基于低场景多样性的小规模数据集,从固定相机捕获,并且缺乏其他传感器输入,如惯性,无线电或深度数据。此外,它们的地基真值(GT)精度大多不足以满足AR的要求。为了缩小这一差距,我们引入了LaMAR,这是一种新的基准,具有全面的捕获和GT管道,可在大型无约束场景中共同注册异构AR设备捕获的真实轨迹和传感器流。为了建立精确的GT,我们的管道以全自动的方式将轨迹与激光扫描进行对齐。因此,我们发布了一个使用头戴式和手持AR设备记录的各种大规模场景的基准数据集。我们扩展了几种最先进的方法,以利用特定于ar的设置,并在基准测试中对它们进行评估。这些结果为当前的研究提供了新的见解,并为AR的定位和地图绘制领域的未来工作揭示了有希望的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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