TY - CHAP A1 - Chajan, Eduard A1 - Schulte-Tigges, Joschua A1 - Reke, Michael A1 - Ferrein, Alexander A1 - Matheis, Dominik A1 - Walter, Thomas T1 - GPU based model-predictive path control for self-driving vehicles T2 - IEEE Intelligent Vehicles Symposium (IV) N2 - One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments. KW - Heuristic algorithms KW - Computational modeling KW - model-predictive control KW - GPU KW - autonomous driving Y1 - 2021 SN - 978-1-7281-5394-0 U6 - http://dx.doi.org/10.1109/IV48863.2021.9575619 N1 - 2021 IEEE Intelligent Vehicles Symposium (IV) July 11-17, 2021. Nagoya, Japan SP - 1243 EP - 1248 PB - IEEE ER - TY - JOUR A1 - Booysen, Tracy A1 - Rieger, Michael A1 - Ferrein, Alexander T1 - Towards inexpensive robots for science & technology teaching and education in Africa Y1 - 2011 SN - 978-1-61284-992-8 N1 - AFRICON, 2011 SP - 1 EP - 6 PB - IEEE CY - New York ER - TY - CHAP A1 - Booysen, Tracy A1 - Mathew, Thomas A1 - Knox, Greig A1 - Fong, W. K. A1 - Stüttgen, Marcel A1 - Ferrein, Alexander A1 - Steinbauer, Gerald T1 - The Scarab Project T2 - ICRA 2015 Developing Countries Forum N2 - Urban Search and Rescue (USAR) is an active research field in the robotics community. Despite recent advances for many open research questions, these kind of systems are not widely used in real rescue missions. One reason is that such systems are complex and not (yet) very reliable; another is that one has to be an robotic expert to run such a system. Moreover, available rescue robots are very expensive and the benefits of using them are still limited. In this paper, we present the Scarab robot, an alternative design for a USAR robot. The robot is light weight, humanpackable and its primary purpose is that of extending the rescuer’s capability to sense the disaster site. The idea is that a responder throws the robot to a certain spot. The robot survives the impact with the ground and relays sensor data such as camera images or thermal images to the responder’s hand-held control unit from which the robot can be remotely controlled. Y1 - 2015 ER - TY - JOUR A1 - Beck, Daniel A1 - Buchleitner, Martin A1 - Ferrein, Alexander A1 - Niemüller, Tim A1 - Steinbauer, Gerald T1 - Mostly Harmless & AllemaniACs - mixed innovations Y1 - 2014 SP - 1 EP - 8 ER - TY - CHAP A1 - Arndt, Tobias A1 - Conzen, Max A1 - Elsen, Ingo A1 - Ferrein, Alexander A1 - Galla, Oskar A1 - Köse, Hakan A1 - Schiffer, Stefan A1 - Tschesche, Matteo T1 - Anomaly detection in the metal-textile industry for the reduction of the cognitive load of quality control workers T2 - PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments N2 - This paper presents an approach for reducing the cognitive load for humans working in quality control (QC) for production processes that adhere to the 6σ -methodology. While 100% QC requires every part to be inspected, this task can be reduced when a human-in-the-loop QC process gets supported by an anomaly detection system that only presents those parts for manual inspection that have a significant likelihood of being defective. This approach shows good results when applied to image-based QC for metal textile products. KW - Datasets KW - Neural networks KW - Anomaly detection KW - Quality control KW - Process optimization Y1 - 2023 SN - 9798400700699 U6 - http://dx.doi.org/10.1145/3594806.3596558 N1 - PETRA '23: Proceedings of the 16th International Conference on Pervasive Technologies Related to Assistive Environments, Corfu Greece, July 5 - 7, 2023. SP - 535 EP - 542 PB - ACM ER - TY - CHAP A1 - Alhwarin, Faraj A1 - Schiffer, Stefan A1 - Ferrein, Alexander A1 - Scholl, Ingrid T1 - Optimized KinectFusion Algorithm for 3D Scanning Applications T2 - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING Y1 - 2018 SN - 978-989-758-278-3 U6 - http://dx.doi.org/10.5220/0006594700500057 SP - 50 EP - 57 ER - TY - CHAP A1 - Alhwarin, Faraj A1 - Schiffer, Stefan A1 - Ferrein, Alexander A1 - Scholl, Ingrid T1 - An Optimized Method for 3D Body Scanning Applications Based on KinectFusion T2 - Communications in Computer and Information Science Y1 - 2019 U6 - http://dx.doi.org/10.1007/978-3-030-29196-9_6 SN - 1865-0929 N1 - 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018; Funchal; Portugal; 19 January 2018 through 21 January 2018 VL - 1024 SP - 100 EP - 113 PB - Springer ER - TY - JOUR A1 - Alhwarin, Faraj A1 - Ferrein, Alexander A1 - Scholl, Ingrid T1 - IR stereo kinect: improving depth images by combining structured light with IR stereo Y1 - 2014 SP - 1 EP - 9 ER - TY - CHAP A1 - Alhwarin, Faraj A1 - Ferrein, Alexander A1 - Scholl, Ingrid T1 - IR stereo kinect: improving depth images by combining structured light with IR stereo T2 - PRICAI 2014: Trends in artificial intelligence : 13th Pacific Rim International Conference on Artificial Intelligence : Gold Coast, QLD, Australia, December 1-5, 2014 : proceedings. (Lecture notes in computer science ; vol. 8862) N2 - RGB-D sensors such as the Microsoft Kinect or the Asus Xtion are inexpensive 3D sensors. A depth image is computed by calculating the distortion of a known infrared light (IR) pattern which is projected into the scene. While these sensors are great devices they have some limitations. The distance they can measure is limited and they suffer from reflection problems on transparent, shiny, or very matte and absorbing objects. If more than one RGB-D camera is used the IR patterns interfere with each other. This results in a massive loss of depth information. In this paper, we present a simple and powerful method to overcome these problems. We propose a stereo RGB-D camera system which uses the pros of RGB-D cameras and combine them with the pros of stereo camera systems. The idea is to utilize the IR images of each two sensors as a stereo pair to generate a depth map. The IR patterns emitted by IR projectors are exploited here to enhance the dense stereo matching even if the observed objects or surfaces are texture-less or transparent. The resulting disparity map is then fused with the depth map offered by the RGB-D sensor to fill the regions and the holes that appear because of interference, or due to transparent or reflective objects. Our results show that the density of depth information is increased especially for transparent, shiny or matte objects. Y1 - 2014 SN - 978-3-319-13559-5 (Print) ; 978-3-319-13560-1 (E-Book) U6 - http://dx.doi.org/10.1007/978-3-319-13560-1_33 SP - 409 EP - 421 PB - Springer CY - München ER - TY - CHAP A1 - Alhwarin, Faraj A1 - Ferrein, Alexander A1 - Scholl, Ingrid T1 - CRVM: Circular Random Variable-based Matcher - A Novel Hashing Method for Fast NN Search in High-dimensional Spaces T2 - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2018 Y1 - 2018 SN - 978-989-758-276-9 U6 - http://dx.doi.org/10.5220/0006692802140221 SP - 214 EP - 221 ER -