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The master's project, abbreviated hence forth as “GPUServices”, fits in the context of research and development of three-dimensional sensor data processing methods applied to mobile robotics. Such methods will be called services in this project, which include a 3D point cloud preprocessing algorithms with data segmentation, separation and identification of planar areas (ground track), and also detecting elements of interest (borders, barriers). Due to the large amount of data to be processed in a short time, these services should use parallel processing, using the GPU to perform partial or complete processing of these data. The application area in focus in this project aims to provide services for an ADAS system: autonomous and intelligent vehicles, forcing them to get close to a real-time processing system due to the autonomous direction of context.The services are divided into stages according to the project methodology, but always striving for acceleration using inherent parallelism: The pre-project consists of organizing an environment for development that is able to coordinate all used technologies, to exploit parallelism and to be integrated to the system already used by the autonomous car; The first service has a responsibility to intelligently extract sensor data that will be used by the project (Laser sensor Velodyne multi-beam), it appears necessary because of the many reading errors and the receiving data format, hence providing data in a matrix structure; The second service, in cooperation with the above, corrects the spatial destabilization due to the sensor fixing base not perfectly parallel to the ground and due to the damping of the vehicle; The third service separates the environment into semantics areas such as ground plane and regions below and above the ground; The fourth service, similar to the above, performs a pre-segmentation of street cruds; The fifth service performs an environmental objects segmentation, separating them into blobs; The sixth service uses all prior to detection and segmentation of street guides.The received sensor data is structured in the form of a cloud of points. They allow processing with great potential for exploitation of parallelism based on the location of the information. However, its major difficulty is the high rate of data received from the sensor (around 700,000 points/sec), and this gives the motivation of this project: to use the full potential of sensor to efficiently use the parallelism of GPU programming, therefore providing data processing services to the user, providing services that helps and make the implementation of ADAS systems easier and/or faster.

Aceleração por GPU de serviços em sistemas robóticos focado no processamento de tempo real de nuvem de pontos 3D