# Hilti SLAM Challenge 2022 The Hilti SLAM Challenge 2022 recordings, as native `.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. A surveyor measured reference positions along every run, and three runs also carry a continuous reference trajectory. Camera frames are published at 10 Hz, on the LiDAR’s clock, rather than the 40 Hz the bags record. `exp23_the_sheldonian_slam` is one run stored as three episodes sharing a `sequence` and differing in `part`, which is why sixteen runs arrive as eighteen episodes. **Details** - Dataset name: `hilti-slam-challenge-2022` - Dataset source: [https://huggingface.co/datasets/Voxel51/Hilti-SLAM-Challenge-2022](https://huggingface.co/datasets/Voxel51/Hilti-SLAM-Challenge-2022) - Dataset size: 49.70 GB - Dataset license: CC-BY-NC-SA-3.0 - Tags: `multimodal, mcap, slam, lidar, imu` - Supported splits: `N/A` - ZooDataset class: [`HiltiSLAMChallenge2022Dataset`](../../api/fiftyone.zoo.datasets.base.md#fiftyone.zoo.datasets.base.HiltiSLAMChallenge2022Dataset) **Example usage** Python CLI ```python import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("hilti-slam-challenge-2022") # The runs with a continuous reference trajectory view = dataset.match({"has_dense_ground_truth": True}) session = fo.launch_app(dataset, view=view) ``` ```shell fiftyone zoo datasets load hilti-slam-challenge-2022 fiftyone app launch hilti-slam-challenge-2022 ```