8,626 research outputs found
Scan matching by cross-correlation and differential evolution
Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time computationally efficient are required to enable location-based services in autonomous mobile devices. Such devices usually have a wide range of high-resolution sensors but only a limited processing power and constrained energy supply. This work introduces a novel high-level scan matching strategy that uses a combination of two advanced algorithms recently used in this field: cross-correlation and differential evolution. The cross-correlation between two laser range scans is used as an efficient measure of scan alignment and the differential evolution algorithm is used to search for the parameters of a transformation that aligns the scans. The proposed method was experimentally validated and showed good ability to match laser range scans taken shortly after each other and an excellent ability to match laser range scans taken with longer time intervals between them.Web of Science88art. no. 85
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
It's the Human that Matters: Accurate User Orientation Estimation for Mobile Computing Applications
Ubiquity of Internet-connected and sensor-equipped portable devices sparked a
new set of mobile computing applications that leverage the proliferating
sensing capabilities of smart-phones. For many of these applications, accurate
estimation of the user heading, as compared to the phone heading, is of
paramount importance. This is of special importance for many crowd-sensing
applications, where the phone can be carried in arbitrary positions and
orientations relative to the user body. Current state-of-the-art focus mainly
on estimating the phone orientation, require the phone to be placed in a
particular position, require user intervention, and/or do not work accurately
indoors; which limits their ubiquitous usability in different applications. In
this paper we present Humaine, a novel system to reliably and accurately
estimate the user orientation relative to the Earth coordinate system.
Humaine requires no prior-configuration nor user intervention and works
accurately indoors and outdoors for arbitrary cell phone positions and
orientations relative to the user body. The system applies statistical analysis
techniques to the inertial sensors widely available on today's cell phones to
estimate both the phone and user orientation. Implementation of the system on
different Android devices with 170 experiments performed at different indoor
and outdoor testbeds shows that Humaine significantly outperforms the
state-of-the-art in diverse scenarios, achieving a median accuracy of
averaged over a wide variety of phone positions. This is
better than the-state-of-the-art. The accuracy is bounded by the error in the
inertial sensors readings and can be enhanced with more accurate sensors and
sensor fusion.Comment: Accepted for publication in the 11th International Conference on
Mobile and Ubiquitous Systems: Computing, Networking and Services
(Mobiquitous 2014
- …