Corrective algorithms for measurement improvement in MScMS-II (Mobile Spatial coordinate Measurement System)

This paper presents a set of algorithms for the correction of measurement errors of a prototype system designed for Large Scale Dimensional Metrology (LSDM) applications. The system, developed in the Quality and Industrial Metrology Laboratory of Politecnico di Torino, is based on the principles of photogrammetry and consists of a set of cameras wirelessly connected to a central unit able to track the position of a portable contact probe. Due to its architecture the system is affected by several systematic error sources. This paper addresses some of them: the distortion of the lenses, the dimension of the probe tip and the kinematic of the probe. By means of the implementation of appropriate mathematical correction models, the overall system performance is significantly improved as shown by the conducted test

External localization system for mobile robotics

We present a fast and precise vision-based software intended for multiple robot localization. The core component of the proposed localization system is an efficient method for black and white circular pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision, and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost camera, its core algorithm is able to process hundreds of images per second while tracking hundreds of objects with millimeter precision. We propose a mathematical model of the method that allows to calculate its precision, area of coverage, and processing speed from the camera’s intrinsic parameters and hardware’s processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions are verified in several experiments. Apart from the method description, we also publish its source code; so, it can be used as an enabling technology for various mobile robotics problems

MScMS-II: an innovative IR-based indoor coordinate measuring system for large-scale metrology applications

According to the current great interest concerning large-scale metrology applications in many different fields of manufacturing industry, technologies and techniques for dimensional measurement have recently shown a substantial improvement. Ease-of-use, logistic and economic issues, as well as metrological performance are assuming a more and more important role among system requirements. This paper describes the architecture and the working principles of a novel infrared (IR) optical-based system, designed to perform low-cost and easy indoor coordinate measurements of large-size objects. The system consists of a distributed network-based layout, whose modularity allows fitting differently sized and shaped working volumes by adequately increasing the number of sensing units. Differently from existing spatially distributed metrological instruments, the remote sensor devices are intended to provide embedded data elaboration capabilities, in order to share the overall computational load. The overall system functionalities, including distributed layout configuration, network self-calibration, 3D point localization, and measurement data elaboration, are discussed. A preliminary metrological characterization of system performance, based on experimental testing, is also presente

Real-time and low-cost embedded platform for car's surrounding vision system

The design and the implementation of a flexible and low-cost embedded system for real-time car's surrounding vision is presented. The target of the proposed multi-camera vision system is to provide the driver a better view of the objects that surround the vehicle. Fish-eye lenses are used to achieve a larger Field of View (FOV) but, on the other hand, introduce radial distortion of the images projected on the sensors. Using low-cost cameras there could be also some alignment issues. Since these complications are noticeable and dangerous, a real-time algorithm for their correction is presented. Then another real-time algorithm, used for merging 4 camera video streams together in a single view, is described. Real-time image processing is achieved through a hardware-software platform

Algorithms for trajectory integration in multiple views

PhDThis thesis addresses the problem of deriving a coherent and accurate localization of moving objects from partial visual information when data are generated by cameras placed in di erent view angles with respect to the scene. The framework is built around applications of scene monitoring with multiple cameras. Firstly, we demonstrate how a geometric-based solution exploits the relationships between corresponding feature points across views and improves accuracy in object location. Then, we improve the estimation of objects location with geometric transformations that account for lens distortions. Additionally, we study the integration of the partial visual information generated by each individual sensor and their combination into one single frame of observation that considers object association and data fusion. Our approach is fully image-based, only relies on 2D constructs and does not require any complex computation in 3D space. We exploit the continuity and coherence in objects' motion when crossing cameras' elds of view. Additionally, we work under the assumption of planar ground plane and wide baseline (i.e. cameras' viewpoints are far apart). The main contributions are: i) the development of a framework for distributed visual sensing that accounts for inaccuracies in the geometry of multiple views; ii) the reduction of trajectory mapping errors using a statistical-based homography estimation; iii) the integration of a polynomial method for correcting inaccuracies caused by the cameras' lens distortion; iv) a global trajectory reconstruction algorithm that associates and integrates fragments of trajectories generated by each camera