Multi-Camera LiDAR Inertial Extension to the Newer College Dataset

TL;DR

This paper introduces an expanded multi-camera LiDAR inertial dataset based on the Newer College Dataset, featuring synchronized hardware data collection, diverse environments, and challenging scenarios for testing localization and mapping algorithms.

Contribution

The paper provides a new, expanded dataset with hardware-synchronized multi-camera, LiDAR, and inertial data, including ground truth poses and diverse challenging environments.

Findings

Dataset includes 4.5 km walking sequences with diverse environments.

Provides ground truth poses at 10 Hz for accurate localization.

Demonstrates use case with multi-camera visual-inertial odometry.

Abstract

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/