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:同步本地化和绘图(SLAM)正在实际应用中部署,尽管许多最先进的解决方案在许多常见情景中仍然难以找到。推进SLM研究的关键需要是提供高质量的数据集以及公平透明的基准。为此,我们创建了Hilti-Oxford数据集(SLAM),以将最先进的SLM系统推向极限。数据集有各种各样的挑战,从稀有和定期的建筑场地到17世纪新古典建筑,有细微细节和曲线表层。为了鼓励多式SLM方法,我们设计了一个数据收集平台的序列,包括一个激光雷达、5个相机和一个IMU(Iartal测量股)等。为了对准确性和稳健的任务进行基准的SLM算法,我们实施了一个新的地面收集方法,使我们的数据集能够准确测量SLM的误差。为了进一步确保准确性,我们的平台的末端序列得到了一个微米精确度扫描仪的验证,我们平台的末级校准程序也用一个更精确的顺序设计了一个数据收集平台的顺序,而时间校准的序列收集了以利达、5个摄像像仪和2版的难度更精确的模型,同时对了我们六月版的实地数据,对20版的实地数据做了一个高版本的实地数据进行了分析,使3号的实地数据,从而完成了了实地数据,对20版的实地数据进行了实地测算。</s>