Fredrik Ekstrand

GIMME - A General Image Multiview Manipulation Engine

This paper presents GIMME (General Image Multiview Manipulation Engine), a highly flexible reconfigurable stand-alone mobile two-camera vision platform with stereo-vision capability. GIMME relies on reconfigurable hard-ware (FPGA) to perform application-specific low to medium-level image-processing at video-rate. The Qseven-extension enables additional processing power. Thanks to its compact design, low power consumption and standardized interfaces (power and communication), GIMME is an ideal vision platform for autonomous and mobile robot applications.

Two camera system for robot applications; navigation

Current approaches to feature detection and matching in images strive to increase the repeatability of the detector and minimize the degree of outliers in the matching. In this paper we present a new approach; we suggest that a lower performance feature detector can produce a result more than adequate for robot navigation irrespectively of the amount of outliers. By using an FPGA together with two cameras we can remove the need for descriptors by performing what we call spurious matching and the use of 3D landmarks. The approach bypasses the problem of outliers and reduces the time consuming task of data association, which slows many matching algorithms down.

An embedded stereo vision module for 6D pose estimation and mapping

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.

Stereo vision based navigation for automated vehicles in industry

This paper proposes a stereo vision based localization and mapping strategy for vehicular navigation within industrial environments using natural landmarks. The work proposed is strictly related to factory automation, since focus is on industrial vehicle autonomous navigation for material handling, in order to increase the operating efficiency with reduced risk for accidents. The stereovision system, proposed as the main sensor, provides the necessary feedback to navigate and simultaneously calibrate the stereocamera parameters (like the camera separation, focal length, camera placement with respect to the robot, etc.). It uses the natural landmarks already present in the environment without additional infrastructures. Some simulation and experimental results are presented in order to explain the proposed method and current status.

An embedded stereo vision module for industrial vehicles automation

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.

Resource limited hardware-based stereo matching for high-speed vision system

This paper proposes a 1-dimensional implementation of area-based stereo matching with minimal resource utilization. It achieves an acceptable disparity map without the use of expensive resources. The matching accuracy for the approach can in some extent even outperform that of its 2-dimensional counterpart. Additionally, as it excels in terms of frame rate and resource utilization, it is highly suitable for real-time stereo-vision systems.

Navigation in a Box: Stereovision for Industry Automation

The research presented addresses the emerging topic of AGVs (Automated Guided Vehicles) specifically related to industrial sites. The work presented has been carried out in the frame of the MALTA project (Multiple Autonomous forklifts for Loading and Transportation Applications), a joint research project between industry and university, funded by the European Regional Development and Robotdalen, in partnership with theSwedish Knowledge Foundation. The project objective is to create fully autonomous forklift trucks for paper reel handling. The result is expected to be of general benefit for industries that use forklift trucks in their material handling through higher operating efficiency and better flexibility with reduced risk for accidents and handling damages than if only manual forklift trucks are used. A brief overview of the state of the art in AGVs will be reported in order to better understand the new challenges and technologies. Among the emerging technologies used for vehicle automation, vision is one of the most promising in terms of versatility and efficiency, with a high potential to drastically reduce the costs.

High-speed segmentation-driven high-resolution matching

This paper proposes a segmentation-based approach for matching of high-resolution stereo images in real time. The approach employs direct region matching in a raster scan fashion influenced by scanline approaches, but with pixel decoupling. To enable real-time performance it is implemented as a heterogeneous system of an FPGA and a sequential processor. Additionally, the approach is designed for low resource usage in order to qualify as part of unified image processing in an embedded system.

Towards an Embedded Real-Time High Resolution Vision System

This paper proposes an approach to image processing for high performance vision systems. Focus is on achieving a scalable method for real-time disparity estimation which can support high resolution images and large disparity ranges. The presented implementation is a non-local matching approach building on the innate qualities of the processing platform which, through utilization of a heterogeneous system, combines low-complexity approaches into performing a high-complexity task. The complementary platform composition allows for the FPGA to reduce the amount of data to the CPU while at the same time promoting the available informational content, thus both reducing the workload as well as raising the level of abstraction. Together with the low resource utilization, this allows for the approach to be designed to support advanced functionality in order to qualify as part of unified image processing in an embedded system.

GIMME2 - an embedded system for stereo vision and processing of megapixel images with FPGA-acceleration

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.