The Newer College Dataset: Handheld LiDAR, Inertial and Vision with Ground Truth
This dataset addresses the need for high-precision ground truth in mobile mapping for researchers in robotics and computer vision, though it is incremental as it builds on existing datasets by adding more accurate ground truth.
The paper introduces a large handheld dataset with LiDAR, inertial, and vision sensors collected over 2.2 km, providing centimeter-accurate 6-DoF ground truth from a survey-grade LiDAR map to enable systematic evaluation of localization and mapping systems.
In this paper we present a large dataset with a variety of mobile mapping sensors collected using a handheld device carried at typical walking speeds for nearly 2.2 km through New College, Oxford. The dataset includes data from two commercially available devices - a stereoscopic-inertial camera and a multi-beam 3D LiDAR, which also provides inertial measurements. Additionally, we used a tripod-mounted survey grade LiDAR scanner to capture a detailed millimeter-accurate 3D map of the test location (containing $\sim$290 million points). Using the map we inferred centimeter-accurate 6 Degree of Freedom (DoF) ground truth for the position of the device for each LiDAR scan to enable better evaluation of LiDAR and vision localisation, mapping and reconstruction systems. This ground truth is the particular novel contribution of this dataset and we believe that it will enable systematic evaluation which many similar datasets have lacked. The dataset combines both built environments, open spaces and vegetated areas so as to test localization and mapping systems such as vision-based navigation, visual and LiDAR SLAM, 3D LIDAR reconstruction and appearance-based place recognition. The dataset is available at: ori.ox.ac.uk/datasets/newer-college-dataset