Hilti SLAM 挑战赛 2023：面向建筑场景中跨传感器组合的单次与多次会话 SLAM 基准评测
Hilti SLAM Challenge 2023: Benchmarking Single + Multi-session SLAM across Sensor Constellations in Construction
AI总结：
本文提出 Hilti SLAM 挑战赛 2023 数据集与基准，用于评测跨传感器组合的单次与多次会话 SLAM，并设计了新型基准标记及毫米级位置估计算法，结果显示单次会话 SLAM 精度提升但多次会话 SLAM 参与较少。
摘要：
同时定位与建图（Simultaneous Localization and Mapping，SLAM）系统是手持设备和机器人应用中实现定位的关键使能技术。过去几年举办的 Hilti SLAM 挑战赛已成功以高精度基准评测了世界上一些最优秀的 SLAM 系统。然而，这些系统还有更多能力有待探索，例如在不同传感器套件之间的平台无关性以及多次会话 SLAM。这些因素间接反映了系统在真实应用中的鲁棒性和部署便捷性。目前公开可用的数据集与基准评测组合中，尚不存在同时考虑这些因素的资源。Hilti SLAM 挑战赛 2023 数据集与基准评测正是为解决这一问题而提出。此外，我们提出了一种新型基准标记（fiducial marker）设计，使地面上预先测量的点能够被安装在机器人上的现成 LiDAR 观测到，并提出一种算法以毫米级精度估计其位置。挑战赛结果表明，整体参与度有所提升，单次会话 SLAM 系统变得越来越精确，并能在不同传感器套件间成功运行，但进行多次会话 SLAM 的参与者相对较少。数据集网址：https://www.hilti-challenge.com/dataset-2023.html
英文摘要：
Simultaneous Localization and Mapping systems are a key enabler for positioning in both handheld and robotic applications. The Hilti SLAM Challenges organized over the past years have been successful at benchmarking some of the world's best SLAM Systems with high accuracy. However, more capabilities of these systems are yet to be explored, such as platform agnosticism across varying sensor suites and multi-session SLAM. These factors indirectly serve as an indicator of robustness and ease of deployment in real-world applications. There exists no dataset plus benchmark combination publicly available, which considers these factors combined. The Hilti SLAM Challenge 2023 Dataset and Benchmark addresses this issue. Additionally, we propose a novel fiducial marker design for a pre-surveyed point on the ground to be observable from an off-the-shelf LiDAR mounted on a robot, and an algorithm to estimate its position at mm-level accuracy. Results from the challenge show an increase in overall participation, single-session SLAM systems getting increasingly accurate, successfully operating across varying sensor suites, but relatively few participants performing multi-session SLAM. Dataset URL: https://www.hilti-challenge.com/dataset-2023.html
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
