New publication: “Trajectory Planning of a Moving Robot Empowers 3D Localization of RFID Tags with a Single Antenna.“

A new publication named “Trajectory Planning of a Moving Robot Empowers 3D Localization of RFID Tags with a Single Antenna.“ has been published in the IEEE Journal of Radio Frequency Identification.

In this work, we present a method for 3D localization of RFID tags by a reader-equipped robot with a single antenna. The robot carries a set of sensors, which enable it to create a map of the environment and locate itself in it (Simultaneous Localization and Mapping -SLAM). Then we exploit the collected phase measurements to localize large tag populations in real-time. We show that by forcing the robot to move along non-straight trajectories, thus creating non-linear synthetic apertures, the circular ambiguity of the possible tag’s locations is eliminated and 3D localization is accomplished. A reliability metric is introduced, suitable for real-time assessment of the localization error. We investigate how the curvature of the robot’s trajectory affects the accuracy under varying multipath conditions. It is found that increasing the trajectory’s slope and number of turns improves the accuracy of the method. We introduce a phase model that accounts for the effects of multipath and derive the closed form expression of the resultant’s phase probability density function. Finally, the proposed method is extended when multiple antennas are available. Experimental results in a “multipath-rich” indoor environment demonstrate a mean 3D error of 35cm, achieved in a few seconds.

Related projects: RELIEF


New book chapter: “Robotics Meets RFID for Simultaneous Localization (of Robots and Objects) and Mapping(SLAM) – A Joined Problem”

A new book chapter named “Robotics Meets RFID for Simultaneous Localization (of Robots and Objects) and Mapping(SLAM) – A Joined Problem” has been published in the Wireless Power Transmission for Sustainable Electronics from Wiley, from Antonis G. Dimitriou, Stavroula Siachalou, Emmanouil Tsardoulias, and Loukas Petrou. This publication is focused on deploying a moving robotic platform, i.e. a robot, which hosts radio frequency identification (RFID) equipment and aims to locate passive RFID tags attached to objects in the surrounding area. The robot hosts additional sensors, namely lidar and depth cameras, enabling it to perform SLAM – simultaneous localization (of its own location) and mapping of any (including previously unknown) area. This work has been performed in the context of the Relief project. Read more in the link below!

New publication: “SLAM for autonomous planetary rovers with global localization”

A new publication named “SLAM for autonomous planetary rovers with global localization” has been published in the Journal of Field Robotics, This publication describes the thesis performed from Dimitrios Geromichalos, with the collaboration of ESA (European Space Agency) and our team. This paper describes a novel approach to simultaneous localization and mapping (SLAM) techniques applied to the autonomous planetary rover exploration scenario to reduce both the relative and absolute localization errors, using two well‐proven techniques: particle filters and scan matching. You can read the full text in the link below.

New publication: “Quantitative and Qualitative Evaluation of ROS-Enabled Local and Global Planners in 2D Static Environments” 21/10/2019

Apart from perception, one of the most fundamental aspects of an autonomous mobile robot is the ability to adequately and safely traverse the environment it operates in. This ability is called Navigation and is performed in a two- or three-dimensional fashion, except for cases where the robot is neither a ground vehicle nor articulated (e.g. robotics arms). The planning part of navigation comprises a global planner, suitable for generating a path from an initial to a target pose, and a local planner tasked with traversing the aforementioned path while dealing with environmental, sensorial and motion uncertainties. However, the task of selecting the optimal global and/or local planner combination is quite hard since no research provides insight on which is best regarding the domain and planner limitations. In this context, current work performs a comparative analysis on qualitative and quantitative aspects of the most common ROS-enabled global and local planners for robots operating in two-dimensional static environments, on the basis of mission-centered and planner-related metrics, optimality and traversability aspects, as well as non-measurable aspects, such as documentation quality, parameterisability, ease of use, etc.

New publication: “A Comparative Analysis of Pattern Matching Techniques Towards OGM Evaluation” 11/7/2019

The alignment of two occupancy grid maps generated by SLAM algorithms is a quite researched problem, being an obligatory step either for unsupervised map merging techniques or for evaluation of OGMs (Occupancy Grid Maps) against a blueprint of the environment. This paper provides an overview of the existing automatic alignment techniques of two occupancy grid maps that employ pattern matching. Additionally, an alignment pipeline using local features and image descriptors is implemented, as well as a method to eliminate erroneous correspondences, aiming at producing the correct transformation between the two maps. Finally, map quality metrics are proposed and utilized, in order to quantify the produced map’s correctness. A comparative analysis was performed over a number of image processing and OGM-oriented detectors and descriptors, in order to identify the best combinations for the map evaluation problem, performed between two OGMs or between an OGM and a Blueprint map.