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.

翻译：同步定位与地图构建（SLAM）技术已在实际应用中部署，然而许多现有先进解决方案在常见场景中仍面临挑战。推动SLAM研究的关键要素之一是高质量数据集的可用性以及公平透明的基准测试。为此，我们创建了希尔蒂-牛津数据集，旨在将当前最先进的SLAM系统推向极限。该数据集涵盖从稀疏规整的建筑工地到具有精细细节和曲面的17世纪新古典主义建筑等多种挑战场景。为鼓励多模态SLAM方法研究，我们设计了集成激光雷达、五台摄像机及惯性测量单元（IMU）的数据采集平台。针对精度与鲁棒性至关重要的SLAM算法基准测试任务，我们提出了新型真值采集方法，使该数据集能以毫米级精度准确测量SLAM位姿误差。为进一步确保精度，平台外参经微米级扫描仪验证，时间校准通过硬件时钟同步实现在线管理。该数据集的多模态性与多样性吸引了大量学术界与工业界研究者参与2022年6月落幕的第二届希尔蒂SLAM挑战赛。挑战结果表明，前三名团队在部分序列中可实现2厘米或更高精度，但在更复杂序列中性能显著下降。