Hilti-Oxford数据集：用于同时定位与建图的毫米级精度基准
Hilti-Oxford Dataset: A Millimetre-Accurate Benchmark for Simultaneous Localization and Mapping
AI总结：
该研究提出Hilti-Oxford数据集，一个毫米级精度的多模态SLAM基准，包含多样挑战场景，用于评估和推动SLAM算法在精度与鲁棒性上的极限。
摘要：
同时定位与建图（SLAM）正被部署于实际应用中，然而许多最先进的解决方案在众多常见场景下仍面临挑战。推进SLAM研究的一个关键需求是获得高质量数据集以及公平透明的基准测试。为此，我们创建了Hilti-Oxford数据集，以将最先进的SLAM系统推向其极限。该数据集包含多种挑战，从稀疏且规则的建筑工地到一座具有精细细节和曲面的17世纪新古典主义建筑。为鼓励多模态SLAM方法，我们设计了一个数据采集平台，配备激光雷达、五个相机和一个IMU（惯性测量单元）。以对精度和鲁棒性至关重要的任务进行SLAM算法基准测试为目标，我们实现了一种新颖的地面真值采集方法，使我们的数据集能够以毫米级精度准确测量SLAM位姿误差。为进一步确保精度，我们平台的相机外参通过微米级精度的扫描仪进行了验证，时间校准通过硬件时间同步在线管理。我们数据集的多模态性和多样性吸引了大量学术界和工业界研究人员参加于2022年6月结束的第二届Hilti SLAM挑战赛。挑战赛结果显示，尽管排名前三的队伍在某些序列上能达到2厘米或更高的精度，但在更困难的序列上性能有所下降。
英文摘要：
Simultaneous Localization and Mapping (SLAM) is being deployed in real-world applications, however many state-of-the-art solutions still struggle in many common scenarios. A key necessity in progressing SLAM research is the availability of high-quality datasets and fair and transparent benchmarking. To this end, we have created the Hilti-Oxford Dataset, to push state-of-the-art SLAM systems to their limits. The dataset has a variety of challenges ranging from sparse and regular construction sites to a 17th century neoclassical building with fine details and curved surfaces. To encourage multi-modal SLAM approaches, we designed a data collection platform featuring a lidar, five cameras, and an IMU (Inertial Measurement Unit). With the goal of benchmarking SLAM algorithms for tasks where accuracy and robustness are paramount, we implemented a novel ground truth collection method that enables our dataset to accurately measure SLAM pose errors with millimeter accuracy. To further ensure accuracy, the extrinsics of our platform were verified with a micrometer-accurate scanner, and temporal calibration was managed online using hardware time synchronization. The multi-modality and diversity of our dataset attracted a large field of academic and industrial researchers to enter the second edition of the Hilti SLAM challenge, which concluded in June 2022. The results of the challenge show that while the top three teams could achieve an accuracy of 2cm or better for some sequences, the performance dropped off in more difficult sequences.
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。
