DORA@CRAS

In the context of the MSc thesis entitled "A Machine Learning Approach for Predicting Docking-based Structures", the dataset denominated DOcking-based stRuctures dAtaset (DORA) was created to provide information for the training of a Deep Learning Network which aimed to detect any docking platforms present in unstructured maritime environments. The dataset was acquired in 3D simulator Gazebo. Different types of docking-based structures were placed in a simulated maritime environment along with other types of objects such as floating buoys and boats. A model of an Autonomous Surface Vehicle (ASV), with a set of incorporated sensors (LiDAR, left and right cameras, GPS, IMU) was sequentially positioned in full circles of increasing distance around the dock. To further increase variety in the dataset, the orientation of the vehicle also varied, by modifying the Euler angles roll, pitch and yaw. This collection process resulted in a dataset of circa 34000 instances, 19000 of which contained a docking plaform and 15000 did not.

데이터와 리소스

추가 정보

필드 값
저자 Maria Inês Pereira
최종 업데이트 6월 4, 2020, 14:16 (UTC)
생성됨 5월 21, 2020, 17:30 (UTC)
CiteAs PEREIRA, M.I., PINTO, A.M., LEITE, P.N. DORA@CRAS [dataset]. 21 may 2020. INESC TEC research data repository. DOI: 10.25747/xpj9-2j17
DOI https://doi.org/10.25747/xpj9-2j17
dc.Contributor Andry Maykol Pinto; Pedro Nuno Leite
dc.Coverage.Spatial CRAS, INESC TEC, Porto, Portugal
dc.Coverage.Temporal March 4th - 8th, 2020
dc.Date February 26th, 2020
dc.Format *.zip, *.txt, *.png, *.pcd
dc.Format.Extent 61GB
dc.Language EN
dc.Publisher INESC TEC
dc.Relation Detecting Docking-based Structures for Persistent ASVs using a Volumetric Neural Network
dc.Type 3D LiDAR scans (.pcd), images from left and right camera (.png) and GPS+IMU information (.txt)
ddi.InstrumentName Velodyne HDL-64E
ddi.InstrumentType simulated LiDAR
ddi.Software Software for data acquisition: ROS (C++), Gazebo