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Multi-Camera LiDAR Inertial Extension to the Newer College Dataset

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arxiv 2112.08854 v3 pith:FJUL3OX5 submitted 2021-12-16 cs.RO cs.CV

Multi-Camera LiDAR Inertial Extension to the Newer College Dataset

Lintong Zhang , Marco Camurri , David Wisth , Maurice Fallon This is my paper
classification cs.RO cs.CV
keywords datasetlidarmulti-cameracollegeexpansioninertialnewerabrupt
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present a multi-camera LiDAR inertial dataset of 4.5 km walking distance as an expansion of the Newer College Dataset. The global shutter multi-camera device is hardware synchronized with both the IMU and LiDAR, which is more accurate than the original dataset with software synchronization. This dataset also provides six Degrees of Freedom (DoF) ground truth poses at LiDAR frequency (10 Hz). Three data collections are described and an example use case of multi-camera visual-inertial odometry is demonstrated. This expansion dataset contains small and narrow passages, large scale open spaces, as well as vegetated areas, to test localization and mapping systems. Furthermore, some sequences present challenging situations such as abrupt lighting change, textureless surfaces, and aggressive motion. The dataset is available at: https://ori-drs.github. io/newer-college-dataset/

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Cited by 5 Pith papers

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  2. G-EDF-Loc: 3D Continuous Gaussian Distance Field for Robust Gradient-Based 6DoF Localization

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  3. From Distances to Trajectories: Real-Time Signed Distance Function Mapping and Distance-Accelerated Motion Planning for UAVs

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    OREN-Bubble* reconstructs a continuous signed distance field online and plans safe quadrotor trajectories through overlapping collision-free balls, demonstrating onboard flight in real time.

  4. BIEVR-LIO: Robust LiDAR-Inertial Odometry through Bump-Image-Enhanced Voxel Maps

    cs.RO 2026-04 unverdicted novelty 5.0

    BIEVR-LIO improves robustness of LiDAR-inertial odometry by representing maps as voxel-wise oriented height images and sampling points only from geometrically informative regions.

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    cs.RO 2026-03 conditional novelty 5.0

    An open-source robot mapping library estimates signed distance fields and uncertainties online by combining Bayesian Hilbert maps with Gaussian process regression.