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This paper presents GIMME2, an embedded stereovision system, designed to be compact, power efficient, cost effective, and high performing in the area of image processing. GIMME2 features two 10 megapixel image sensors and a Xilinx Zynq, which combines FPGA-fabric with a dual-core ARM CPU on a single chip. This enables GIMME2 to process video-rate megapixel image streams at real-time, exploiting the benefits of heterogeneous processing.

This paper presents results and conclusions from design, manufacturing, and benchmarking of a heterogeneous computing low power fault tolerant computer, realized on an industrial Qseven^® small form factor (SFF) platform. A heterogeneous computer in this context features multi-core processors (CPU), a graphical processing unit (GPU), and a field programmable gate array (FPGA). The x86 compatible CPU enables the use of vast amounts of commonly available software and operating systems, which can be used for space and harsh environments. The developed heterogeneous computer shares the same core architecture as game consoles such as Microsoft Xbox One and Sony Playstation 4 and has an aggregated computational performance in the TFLOP range. The processing power can be used for on-board intelligent data processing and higher degrees of autonomy in general. The module feature quad core 1.5 GHz 64 bit CPU (24 GFLOPs), 160 GPU shader cores (127 GFLOPs), and a 12 Mgate equivalent FPGA fabric with a safety critical ARM® Cortex-M3 MCU.

This paper presents an embedded vision system based on reconfigurable hardware (FPGA) to perform stereo image processing and 3D mapping of sparse features for autonomous navigation and obstacle detection in industrial settings. We propose an EKF based visual SLAM to achieve a 6D localization of the vehicle even in non flat scenarios. The system uses vision as the only source of information. As a consequence, it operates regardless of the odometry from the vehicle since visual odometry is used. © 2013 IEEE.

Typical pick and place, and machine tending applications often require an industrial robot to be embedded in a cell and to communicate with other devices in the cell. Programming the program logic is a tedious job, requiring expert programming knowledge, and it can take more time than programming the specific robot movements itself. We propose a new system, which takes in the description of the whole manufacturing process in natural language as input, fills in the implicit actions, and plans the sequence of actions to accomplish the task described in minimal makespan using a modified partial planning algorithm. Finally we demonstrate that the proposed system can come up with a sensible plan for the given instructions.

The Black Pearl is a custom made autonomous underwater vehicle developed at Mälardalen University, Sweden. It is built in a modular fashion, including its mechanics, electronics and software. After a successful participation at the RoboSub competition in 2012 and winning the prize for best craftsmanship, this year we made minor improvements to the hardware, while the focus of the robot's evolution shifted to the software part. In this paper we give an overview of how the Black Pearl is built, both from the hardware and software point of view.

This paper presents a set of approaches for increasing the accuracy of basic area-based stereo matching methods. It is targeting real-time FPGA systems for dense disparity map estimation. The methods are focused on low resource usage and maximized improvement per cost unit to enable the inclusion of an autonomous system in an FPGA. The approach performs on par with other area-matching implementations, but at substantially lower resource usage. Additionally, the solution removes the requirement for external memory for reconfigurable hardware together with the limitation in image size accompanying standard methods. As a fully piped complete on-chip solution, it is highly suitable for real-time stereo-vision systems, with a frame rate over 100 fps for Megapixel images.

FPGA has been used in many robotics projects for real-time image processing. It provides reliable systems with low execution time and simplified timing analysis. Many of these systems take a lot of time in development and testing phases. In some cases, it is not possible to test the system in real environments very often, due to accessibility, availability or cost problems. This paper is the result of a case study on vision systems for two robotics projects in which the vision team consisted of seven students working for six months fulltime on developing and implementing different image algorithms. While FPGA has been used for real-time image processing, some steps have been taken in order to reduce the development and testing phases. The main focus of the project is to integrate different testing methods with FPGA development. It includes a component based solution that uses a two-way communication with a PC controller for system evaluation and testing. Once the data is acquired from the vision board, the system stores it and simulates the same environment that has been captured earlier by feeding back the obtained data to FPGA. This approach addresses and implements a debugging methodology for FPGA based solutions which accelerate the development phase. In order to transfer massive information of images, RMII which is an interface for Ethernet communication, has been investigated and implemented. The provided solution makes changes easier, saves time and solves the problems mentioned earlier.

Humans employ different information channels (modalities) such as speech, pictures and gestures in their commu- nication. It is believed that some of these modalities are more error-prone to some specific type of data and therefore multimodality can help to reduce ambiguities in the interaction. There have been numerous efforts in implementing multimodal interfaces for computers and robots. Yet, there is no general standard framework for developing them. In this paper we propose a general framework for implementing multimodal interfaces. It is designed to perform natural language understanding, multi- modal integration and semantic analysis with an incremental pipeline and includes a multimodal grammar language, which is used for multimodal presentation and semantic meaning generation.

This paper presents an embedded vision system based on reconfigurable hardware (FPGA) and two CMOS cameras to perform stereo image processing and 3D mapping for autonomous navigation. We propose an EKF based visual SLAM and sparse feature detectors to achieve 6D localization of the vehicle in non flat scenarios. The system can operate regardless of the odometry information from the vehicle since visual odometry is used. As a result, the final system is compact and easy to install and configure.

A versatile General Camera Model, GCM, has been developed, and is described in detail. The model is general in the sense that it can capture both fisheye and conventional as well as catadioptric cameras in a unified framework. The camera model includes efficient handling of non-central cameras as well as compensations for decentring distortion. A novel way of analysing radial distortion functions of camera models leads to a straightforward improvement of conventional models with respect to generality, accuracy and simplicity. Different camera models are experimentally compared for two cameras with conventional and fisheye lenses, and the results show that the overall performance is favourable for the GCM. (C) 2011 Elsevier Inc. All rights reserved.