FGI ARVO VLS-128 LiDAR Point Cloud, Käpylä, 7th of September 2020

This LiDAR point cloud dataset is collected with a research platform of Finnish Geospatial Research Institute (FGI), called Autonomous Research Vehicle Observatory (ARVO). The dataset was collected with Velodyne VLS-128 Alpha Puck LiDAR, 7th of September 2020 in a suburban environment in the area of Käpylä in Helsinki, the capital of Finland. The environment in the dataset consists of a straight two-way asphalt street, called Pohjolankatu, which starts from a larger controlled intersection at the crossing of Tuusulanväylä (60.213326° N, 24.942908° E in WGS84) and passes by three smaller uncontrolled intersections until the crossing of Metsolantie (60.215537° N, 24.950065° E). It is a typical suburban street with tram lines, sidewalks, small buildings, traffic signs, light poles, and cars parked on both sides of the streets. To collect a reference trajectory and to synchronize the LiDAR measurements, we have used a Novatel PwrPak7-E1 GNSS Inertial Navigation System (INS). The motion distortion of each individual scan has been corrected with a postprocessed GNSS INS trajectory and the scans have been registered with Normal Distributions Transform (NDT). Each point is provided with a semantic label probability vector and the final point cloud is averaged with a 1 cm voxel filter. The steps to create this preprocessed dataset have been described in more detail in the article "Towards High-Definition Maps: a Framework Leveraging Semantic Segmentation to Improve NDT Map Compression and Descriptivity" published in IROS 2022. However, the number of points in each semantic segment in Table I in Section IV-A are different. The correct values are shown in the table below. This does not affect the results. TABLE I: RandLA-Net classified dataset label proportions. Semantic label No. of points % of all % of used Ground 14,206,060 32.3 50.3 Building 7,782,757 17.7 27.6 Tree Trunk 3,736,775 8.5 13.2 Fence 2,201,851 5.0 7.8 Pole 206,983 0.5 0.7 Traffic Sign 85,316 0.2 0.3 Labels used here 28,219,742 64.1 100.0 Others 15,821,962 35.9 Total 44,041,704 100.0