Perception plays a crucial role in various robot applications. However,
existing well-annotated datasets are biased towards autonomous driving
scenarios, while unlabelled SLAM datasets are quickly over-fitted, and often
lack environment and domain variations. To expand the frontier of these fields,
we introduce a comprehensive dataset named MCD (Multi-Campus Dataset),
featuring a wide range of sensing modalities, high-accuracy ground truth, and
diverse challenging environments across three Eurasian university campuses. MCD
comprises both CCS (Classical Cylindrical Spinning) and NRE (Non-Repetitive
Epicyclic) lidars, high-quality IMUs (Inertial Measurement Units), cameras, and
UWB (Ultra-WideBand) sensors. Furthermore, in a pioneering effort, we introduce
semantic annotations of 29 classes over 59k sparse NRE lidar scans across three
domains, thus providing a novel challenge to existing semantic segmentation
research upon this largely unexplored lidar modality. Finally, we propose, for
the first time to the best of our knowledge, continuous-time ground truth based
on optimization-based registration of lidar-inertial data on large survey-grade
prior maps, which are also publicly released, each several times the size of
existing ones. We conduct a rigorous evaluation of numerous state-of-the-art
algorithms on MCD, report their performance, and highlight the challenges
awaiting solutions from the research community.

在多个欧亚大学校园中，通过 MCD（多校园数据集），我们引入了一个综合性的数据集，其中包括各种传感模态、高精度的真实地面数据以及不同挑战性环境，与现有的地图进行注册，为感知及机器人领域的研究提供了全新的挑战。