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：同时定位与地图构建（SLAM）技术正在实际应用中得到部署，然而许多现有先进方案在常见场景中仍面临挑战。推动SLAM研究的关键要素在于获取高质量数据集以及开展公平透明的基准测试。为此，我们创建了Hilti-Oxford数据集，旨在挑战顶尖SLAM系统的性能极限。该数据集包含从稀疏规整的建筑工地到具有精细结构与曲面的17世纪新古典主义建筑等多种复杂场景。为支持多模态SLAM方法研究，我们设计并构建了配备激光雷达、五台相机及惯性测量单元（IMU）的数据采集平台。针对要求高精度与强鲁棒性的SLAM算法基准测试需求，我们创新性地提出地面真值采集方法，使数据集能够以毫米级精度测量SLAM位姿误差。为确保精度，平台外参通过微米级精度扫描仪进行验证，时间校准则采用硬件同步方案在线实现。该数据集的多模态特性与场景多样性吸引了大量学术界与工业界研究人员参与2022年6月落幕的第二届Hilti SLAM挑战赛。竞赛结果表明，虽然前三名团队在部分序列上可达2厘米或更优精度，但在更具挑战性的复杂序列中性能显著下降。