Giacomo Spampinato

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.

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.

Object selection using a spatial language for flexible assembly

In this paper we present a new simplified natural language that makes use of spatial relations between the objects in scene to navigate an industrial robot for simple pick and place applications. Developing easy to use, intuitive interfaces is crucial to introduce robotic automation to many small medium sized enterprises (SMEs). Due to their continuously changing product lines, reprogramming costs are far more higher than installation costs. In order to hide the complexities of robot programming we propose a natural language where the use can control and jog the robot based on reference objects in the scene. We used Gaussian kernels to represent spatial regions, such as left or above. Finally we present some dialogues between the user and robot to demonstrate the usefulness of the proposed system.

Bandwidth adaptation in hierarchical scheduling using fuzzy controllers

In our previous work, we have introduced an adaptive hierarchical scheduling framework as a solution for composing dynamic real-time systems, i.e., systems where the CPU demand of their tasks are subjected to unknown and potentially drastic changes during run-time. The framework uses the PI controller which periodically adapts the system to the current load situation. The conventional PI controller despite simplicity and low CPU overhead, provides acceptable performance. However, increasing the pressure on the controller, e.g, with an application consisting of multiple tasks with drastically oscillating execution times, degrades the performance of the PI controller. Therefore, in this paper we modify the structure of our adaptive framework by replacing the PI controller with a fuzzy controller to achieve better performance. Furthermore, we conduct a simulation-based case study in which we compose dynamic tasks such as video decoder tasks with a set of static tasks into a single system, and we show that the new fuzzy controller outperforms our previous PI controller.

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.

Towards robust human robot collaboration in industrial environments

In this paper a system, which is driven through natural language, that allows operators to select and manipulate objects in the environment using an industrial robot is proposed. In order to hide the complexities of robot programming we propose a natural language where the user can control and jog the robot based on reference objects in the scene. We used semantic networks to relate different types of objects in the scene.

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.

Validation of stereo matching for robot navigation

This paper presents results from experiments on visual stereo matching for robot navigation. Visual features are stereo paired with respect to their pixel position. Stereo triangulating all paired visual features results in a set of landmarks whereof a subset are true landmarks. Constraining the horizontal disparity limits the amount of spurious matches. The stereo matching is validated by finding which landmarks survives short motions measured with a complementary navigation system, like odometry, thus transferring the stereo matching problem from two to three dimensional space and robot motion is estimated from the landmarks surviving the motion. The results from our experiments show that the spurious matching algorithm for stereo matching validation works and that the system is able to estimate the motion.