M.P. Muresan, S. Nedevschi, I. Giosan Proceedings of 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 7-9, 2017, pp. 317-322. The robust detection of obstacles, on a given road path by vehicles equipped with range measurement devices represents a requirement for many research fields including autonomous driving and advanced driving assistance systems. One particular sensor system used for measurement tasks, due to its known accuracy, is the LIDAR (Light Detection and Ranging). The commercial price and computational intensiveness of such systems generally increase with the number of scanning layers. For this reason, in...
S.E.C. Goga, S. Nedevschi Proceedings of 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 7-9, 2017, pp. 309-315. This paper proposes a novel approach for segmenting and space partitioning data ofsparse 3D LiDAR point clouds for autonomous driving tasks in urban environments. Our main focus is building a compact data representation which provides enough information for an accurate segmentation algorithm. We propose the use of an extension of elevation maps for automotive driving perception tasks which is capable of dealing with both protruding and hanging objects found in urban scenes like bridges, hanging...
B.C.Z. Blaga, S. Nedevschi Proceedings of 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 7-9, 2017, pp. 295-301. In an autonomous driving system, drift can affect the sensor’s position, introducing errors in the extrinsic calibration. For this reason, we have developed a method which continuously monitors two sensors, camera, and LIDAR with 16 beams, and adjusts the value of their cross-calibration. Our algorithm, starting from correct values of the extrinsic crosscalibration parameters, can detect small sensor drift during vehicle driving, by overlapping the edges from the LIDAR over the edges from the image....
Technical Report 1 Explanation of the work carried out by the beneficiaries and Overview of the progress. pdf
Initial dissemination report The UP-DRIVE dissemination report contains detailed descriptions of the strategy and the means by which UP-DRIVE is communicated. pdf
Evaluation report on integration process and results of first development cycle This report explains the integration process and highlights the results of the first of the two development cycles. pdf
Integration and test tools and processes This deliverable describes the integration tools and the processes established by the consortium. The choice of the tools and the processes is based on best practices from previous collaborative robotic projects. pdf
First development and integration cycle of decision making and navigation This deliverable provides insights of the current software architecture representing the decision-making and navigation framework assigned to WP7 of the UP-Drive project which is eager to create a car capable of self-driving in urban traffic scenarios up to 30 km/h. pdf
First development and integration cycle of scene understanding This deliverable contributes to the Up-Drive project´s endeavor to create a car capable of self-driving in an unconstrained urban environment with speeds up to 30 km/h. D6.2 covers the WP6 (Scene/scenario understanding) related development and the first integration cycle of its implementation into the Up-Drive-wide system. pdf
First development and integration cycle of lifelong mapping This deliverable describes the lifelong mapping framework after the first development & integration cycle. All components, notably the metric and semantic map, the metric online localization, the semantic data aggregation and the map summarization are functional and integrated on the vehicles, fulfill their basic purposes and interact with each other in a limited fashion. All components deliver first evaluation results. pdf
