Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor. LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples. This dataset is the 10m dataset, which is part of the larger LIDAROC dataset. The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters). For the 5m and 20m datasets, please refer to the link below: LIDAROC 5m LIDAROC 20m

To use this dataset, please cite this paper. G. Jati, M. Molan, F. Barchi, A. Bartolini, G. Mercurio and A. Acquaviva, "LIDAROC: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability," in IEEE Sensors Letters, vol. 8, no. 9, pp. 1-4, Sept. 2024, Art no. 1502404, doi: 10.1109/LSENS.2024.3434624