Voxel51/Hilti-SLAM-Challenge-2022

Hilti SLAM Challenge 2022 → FiftyOne (Native Multimodal MCAP)

The Hilti SLAM Challenge 2022 recordings, converted from ROS 1 bags to native multimodal MCAP episodes.

The recordings were made with a handheld rig called Phasma, which carries five synchronized global-shutter cameras, a Hesai PandarXT-32 LiDAR and a Bosch BMI085 IMU. Seven runs were walked through an active construction site in Schaan, Liechtenstein, and nine through the Sheldonian Theatre in Oxford, over five days in March and April 2022. Between them the runs cover construction floors, staircases, long corridors, galleries, basements, an attic, a cupola and the ground outside, and most of them pass through the same place more than once.

A surveyor measured reference positions along every run, and three runs also carry a continuous reference trajectory.

Installation

pip install fiftyone

Usage

import fiftyone as fo
import fiftyone.utils.huggingface as fouh

dataset = fouh.load_from_hub(
    "Voxel51/Hilti-SLAM-Challenge-2022",
    name="Hilti-SLAM-Challenge-2022",
    persistent=True,
)
fo.launch_app(dataset)

The runs that carry a continuous reference trajectory:

view = dataset.match({"has_dense_ground_truth": True})

What you get

Sixteen runs arrive as 18 episodes and 70.7 minutes of recording. Each episode carries:

• /cam0 through /cam4, the five cameras at 720x540, as foxglove.CompressedImage
• /cam0-calibration through /cam4-calibration, the intrinsics for each camera, as foxglove.CameraCalibration
• /lidar-points, the LiDAR at its native 10 Hz, as foxglove.PointCloud with x, y, z, intensity, ring and time_offset
• /imu.plot, three-axis acceleration and angular rate at 400 Hz
• /ground-truth-control-points, the surveyed reference positions, as foxglove.SceneUpdate, labelled with the surveyor's marker names
• /ground-truth-pose, the continuous reference trajectory, as foxglove.PoseInFrame, on the three runs that have one
• /sequence, naming the run

Across the whole set that comes to 212,065 camera frames, 42,408 LiDAR sweeps holding 2.55 billion points, 1,692,948 inertial samples and 159 surveyed reference positions.

Episodes carry the fields sequence, part, site, recorded, scene, cameras, num_camera_frames, num_lidar_scans, num_lidar_points, num_imu_samples, has_dense_ground_truth, num_ground_truth_poses, num_control_points, num_control_points_observed, control_point_names and duration.

Notes on the conversion

Camera frames are published at 10 Hz. The bags record them at 40 Hz. All sixteen runs and all five cameras are present.

Camera frames are JPEG at quality 92. The source frames are uncompressed 8-bit greyscale.

The LiDAR's per-point time is published as time_offset, in seconds relative to the sweep. The source records it as an absolute float64 timestamp, and the point cloud format packs fields as 32-bit floats, which quantize a value near 1.65e9 into steps of about two minutes.

The cameras are fisheye. Their four distortion coefficients are published under the equidistant model name they were calibrated with.

exp23_the_sheldonian_slam is one continuous run stored as three bags. It is published as three episodes that share a sequence value and differ in part. Its surveyed positions are divided between the three by time.

Marker names come from the announcements in the recordings and are matched to surveyed positions by time. 158 of the 159 positions carry a name. One announced marker in exp10_cupola_2 has no surveyed position, and one position in exp23_the_sheldonian_slam part 2 was measured after the cameras stopped.

Reference data is expressed in the IMU frame.

The laser_scans, CAD and calibration directories of the source release are not reproduced here. The camera intrinsics are published with each episode.

License & attribution

The source dataset is released under CC BY-NC-SA 3.0, and this conversion is distributed under the same license. Use is limited to non-commercial purposes, attribution is required, and adaptations must be distributed under the same or a compatible license.

Changes from the source: conversion from ROS 1 bags to the FiftyOne MCAP flavor, JPEG encoding of the camera frames, camera frame rate reduced to 10 Hz, and the LiDAR per-point timestamp rebased to a per-sweep offset.

Citation

@article{zhang2023hilti,
  author  = {Lintong Zhang and Michael Helmberger and Lanke Frank Tarimo Fu and David Wisth and Marco Camurri and Davide Scaramuzza and Maurice Fallon},
  title   = {Hilti-Oxford Dataset: A Millimeter-Accurate Benchmark for Simultaneous Localization and Mapping},
  journal = {IEEE Robotics and Automation Letters},
  volume  = {8},
  number  = {1},
  pages   = {408--415},
  year    = {2023},
  doi     = {10.1109/LRA.2022.3226077}
}