The Hilti SLAM Challenge Dataset

Helmberger, Michael; Morin, Kristian; Kumar, Nitish; Wang, Danwei; Yue, Yufeng; Cioffi, Giovanni; Scaramuzza, Davide

doi:10.1109/lra.2022.3183759

Cited by 54 publications

(22 citation statements)

References 33 publications

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“…The 2021 Hilti Challenge dataset [9] used similar sensors to our proposed dataset and featured an AlphaSense 5-camera module which offered a 270°continuous field of view. For wide-coverage lidar measurements, [9] used the Ouster OS0-64 lidar with a range accuracy of ±3 cm, whereas the dataset presented in this paper uses the much more accurate Hesai PandarXT-32 lidar with ±1 cm range accuracy.…”

Section: Related Workmentioning

confidence: 99%

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Hilti-Oxford Dataset: A Millimeter-Accurate Benchmark for Simultaneous Localization and Mapping

Zhang

Helmberger

et al. 2023

IEEE Robot. Autom. Lett.

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Simultaneous Localization and Mapping (SLAM) is being deployed in real-world applications, however many stateof-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 stateof-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 2 cm or better for some sequences, the performance dropped off in more difficult sequences.

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Section: Related Workmentioning

confidence: 99%

“…Drawing on Hilti's expertise, we manufactured a precise handheld device that had improved accuracy and stability compared the rig used in the previous challenge [9]. Specifically, by using milled components and dowel pins, we ensured an accurate assembly of the sensors.…”

Section: Hardwarementioning

confidence: 99%

Hilti-Oxford Dataset: A Millimeter-Accurate Benchmark for Simultaneous Localization and Mapping

Zhang

Helmberger

et al. 2023

IEEE Robot. Autom. Lett.

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“…Remark: A point set and its coordinate is not a one-to-one mapping. While it is obvious that the coordinate is uniquely determined from the point set as shown in (10), the reverse way does not hold: the point set cannot be recovered from its coordinate uniquely. Since different point sets may lead to the same coordinate, the raw points must be saved if a reclustering is needed.…”

Section: Point Clustermentioning

confidence: 99%

“…where D :,l x ∈ R 3 represents the l-th column of D(x). Finally, substituting U(x) = [u 1 (x) u 2 (x) u 3 (x)] and ( 9) into (10), we obtain…”

Section: Efficient and Consistent Bundle Adjustment Onmentioning

confidence: 99%

“…{u3007335,xliuaa}@connect.hku.hk, fuzhang@hku.hk 2 https://github.com/hku-mars/BALM (DDAA) depth measurements, lidar sensors have the ability to build a dense and accurate 3D map of the environment in realtime and at a relatively low computation cost. These unique advantages have made lidar sensors essential to a variety of applications that require real-time, dense, and accurate 3D mapping of the environment, such as autonomous driving [3,4], unmanned aerial vehicles navigation [5]- [7], and realtime mobile mapping [8]- [10]. This trend becomes even more evident with recent developments in lidar technologies which have enabled the commercialization and mass production of lightweight and high-performance solid-state lidars at a significantly lower cost [11,12].…”

Section: Introductionmentioning

confidence: 99%

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Efficient and Consistent Bundle Adjustment on Lidar Point Clouds

Liu¹,

Liu²,

Zhang³

2022

Preprint

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Bundle Adjustment (BA) refers to the problem of simultaneous determination of sensor poses and scene geometry, which is a fundamental problem in robot vision. This paper presents an efficient and consistent bundle adjustment method for lidar sensors. The method employs edge and plane features to represent the scene geometry, and directly minimizes the natural Euclidean distance from each raw point to the respective geometry feature. A nice property of this formulation is that the geometry features can be analytically solved, drastically reducing the dimension of the numerical optimization. To represent and solve the resultant optimization problem more efficiently, this paper then proposes a novel concept point clusters, which encodes all raw points associated to the same feature by a compact set of parameters, the point cluster coordinates. We derive the closed-form derivatives, up to the second order, of the BA optimization based on the point cluster coordinates and show their theoretical properties such as the null spaces and sparsity. Based on these theoretical results, this paper develops an efficient second-order BA solver. Besides estimating the lidar poses, the solver also exploits the second order information to estimate the pose uncertainty caused by measurement noises, leading to consistent estimates of lidar poses. Moreover, thanks to the use of point cluster, the developed solver fundamentally avoids the enumeration of each raw point (which is very time-consuming due to the large number) in all steps of the optimization: cost evaluation, derivatives evaluation and uncertainty evaluation.The proposed method is extensively evaluated at different levels: consistency, accuracy, and computation efficiency in both simulated and actual environments. Benchmark evaluation on 19 real-world open sequences covering various datasets (Hilti, NTU-VIRAL and UrbanLoco), environments (campus, urban streets, offices, laboratory, and construction sites), lidar types (Ouster OS0-64, Ouster OS1-16, Velodyne HDL 32E), and motion types (handheld, UAV-based, and ground vehicles-based) shows that our method achieves consistently and significantly higher performance than other state-of-the-art counterparts in terms of localization accuracy, mapping quality, and computation efficiency. In particular, our method achieves a mapping accuracy at a level of the lidar measurement noise (i.e., a few centimeters) while processing all sequences in less than half minute on a standard desktop CPU. Finally, we show how our proposed method effectively improves the accuracy and/or computation efficiency of some important robotic techniques, including lidarinertial odometry, multi-lidar extrinsic calibration, and highaccuracy global mapping. The implementation of our method is open sourced to benefit the robotics community and beyond 2 .

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A Novel Topology Metric for Indoor Point Cloud SLAM Based on Plane Detection Optimization

Ouyang,

Cui,

et al. 2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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The Hilti SLAM Challenge Dataset

Cited by 54 publications

References 33 publications

Hilti-Oxford Dataset: A Millimeter-Accurate Benchmark for Simultaneous Localization and Mapping

Hilti-Oxford Dataset: A Millimeter-Accurate Benchmark for Simultaneous Localization and Mapping

Efficient and Consistent Bundle Adjustment on Lidar Point Clouds

A Novel Topology Metric for Indoor Point Cloud SLAM Based on Plane Detection Optimization

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