Correspondenceless scan-to-map-scan matching of homoriented 2D scans for mobile robot localisation

The objective of this study is improving the location estimate of a mobile robot capable of motion on a plane and mounted with a conventional 2D LIDAR sensor, given an initial guess for its location on a 2D map of its surroundings. Documented herein is the theoretical reasoning behind solving a matching problem between two homoriented 2D scans, one derived from the robot's physical sensor and one derived by simulating its operation within the map, in a manner that does not require the establishing of correspondences between their constituting rays. Two results are proved and subsequently shown through experiments. The first is that the true position of the sensor can be recovered with arbitrary precision when the physical sensor reports faultless measurements and there is no discrepancy between the environment the robot operates in and its perception of it by the robot. The second is that when either is affected by disturbance, the location estimate is bound in a neighbourhood of the true location whose radius is proportional to the affecting disturbance.Comment: 19 pages, 19 figure

CBGL: Fast Monte Carlo Passive Global Localisation of 2D LIDAR Sensor

Navigation of a mobile robot is conditioned on the knowledge of its pose. In observer-based localisation configurations its initial pose may not be knowable in advance, leading to the need of its estimation. Solutions to the problem of global localisation are either robust against noise and environment arbitrariness but require motion and time, which may (need to) be economised on, or require minimal estimation time but assume environmental structure, may be sensitive to noise, and demand preprocessing and tuning. This article proposes a method that retains the strengths and avoids the weaknesses of the two approaches. The method leverages properties of the Cumulative Absolute Error per Ray metric with respect to the errors of pose estimates of a 2D LIDAR sensor, and utilises scan--to--map-scan matching for fine(r) pose approximations. A large number of tests, in real and simulated conditions, involving disparate environments and sensor properties, illustrate that the proposed method outperforms state-of-the-art methods of both classes of solutions in terms of pose discovery rate and execution time. The source code is available for download.Comment: 8 pages, 10 figures, 3 algorithm

Robust Decentralized Control of Cooperative Multi-robot Systems : An inter-constraint Receding Horizon approach

In this work, a robust decentralized model predictive control regime for a team of cooperating robot systems is designed. Their assumed dynamics are in continuous time and non-linear. The problem involves agents whose dynamics are independent of one-another, and its solution couples their constraints as a means of capturing the cooperative behaviour required. Analytical proofs are given to show that, under the proposed control regime: (a) Subject to initial feasibility, the optimization solved at each step by each agent will always be feasible, irrespective of whether or not disturbances affect the agents. In the former case, recursive feasibility is established through successive restriction of each agent's constraints during the periodic solution to its respective optimization problem. (b) Each (sub)system can be stabilized to a desired configuration, either asymptotically when uncertainty is absent, or within a neighbourhood of it, when uncertainty is present, thus attenuating the affecting disturbance. In this context, disturbances are assumed to be additive and bounded. Simulations verify the efficacy of the proposed method over a range of different operating environments.I detta arbete, en robust decentraliserad modell prediktiv kontroll regime förett lag av samverkande robotsystem är utformade. Deras antagnat dynamikär i kontinuerlig tid och olinjär. Problemet involverar agenter vars dynamik äroberoende av varandra, och sina lösning kopplar sina begränsningar som ettmedel för att fånga det kooperativa beteendet som krävs. Analytiska bevis gesför att visa att, enligt det föreslagna kontrollsystemet: (a) med förbehåll förförsta genomförbarhet, kommer optimeringen som löses vid varje steg av varjeagent alltid vara möjligt, oavsett huruvida störningar påverkar agenserna ellerinte. I det förre fallet är rekursiv genomförbarhet etablerad genom successivabegränsningar av varje agents inskränkning under den periodiska lösningentill dess respektive optimeringsproblem. (b) Varje (sub) system kan stabiliserastill en önskad konfiguration, antingen asymptotiskt när osäkerhet saknas,eller inom en närhet av det, när osäkerhet är närvarande, således dämparpåverkanstörning. I detta sammanhang antas störningar vara additiv och avgränsas.Simuleringar verifierar effekten av den föreslagna metoden över ettintervall av olika driftsmiljöer

Methods for estimating the pose of a 2D LIDAR sensor through scan–to–map-scan matching

During the last decade mobile robotics has been on the rise in terms of its range of use and content of applications. Autonomous ground and aerial robots are used in warehouses for inventorying and localising products, in hospitals for decontamination purposes, in agriculture for monitoring crops and process automation, and for transporting goods or people. In all cases the self-knowledge of a vehicle’s pose in space, i.e. its position and orientation, is essential for its autonomous motion and the satisfaction of objectives of its mission. In indoor areas, where pose information transmitted via satellite is denied, or contexts where indoor measuring infrastructure is absent, the only alternative is the estimation of the pose vector, and the estimation process is undertaken by the vehicle itself, based on the sensors it is equiped with. The subject of this dissertation is autonomous ground mobile robotics in the context of the absence of pose measurements. Its main content is conserned with the estimation of the pose of a mobile base on the basis of two dimensional measurements derived from a lidar range sensor and the reduction of the estimation error, with the pose estimate being derived from traditional methods of the literature. Specifically we explore ways in which matching measurements from the physical sensor against virtual scans—computed either from the map of the environment or from a second measurement—can be used to satisfy the two above-stated objectives, with performance superior to that of established and state-of-the-art methods of the literature. We first design a methodology for evaluating autonomous navigation algorithms, and through its implementation we observe the existence of pose estimate errors. We then investigate ways of reducing these errors with methods intenal and external to the mechanisms of a particle filter. One of these external ways is prosthetic scan–to–map-scan matching. Discovering that scan-matching is performed in the literature in a manner that is not robust to measurement noise, the magnitude of position estimate errors, and tuning parameters, we propose scan–to–map-scanmatching methods that replace the relevant pathogenic state-of-the-art mechanisms with mechanisms based on the Fourier transform and its properties. At the application level we design systems that given the map of the mobile base environment aim at solving the global localisation problem, and reducing the pose error during pose tracking. Finally, we generalize our approach in the absence of a map in order to generate odometry based on a panoramic lidar’s range measurements.Η ρομποτική κινητής βάσης είναι ένας ολοένα ανερχόμενος κλάδος της ρομποτικής, τόσο ως προς το εύρος των τομέων χρήσης της όσο και ως προς το περιεχόμενο των εφαρμογών της. Αυτόνομα επίγεια και ιπτάμενα ρομπότ χρησιμοποιούνται σε αποθήκες για την απογραφή προϊόντων και την εύρεση της θέσης τους, σε νοσοκομεία για την απολύμανση χώρων, στη γεωργία για παρακολούθηση καλλιεργειών και αυτοματοποίηση διαδικασιών, και για τη μεταφορά εμπορευμάτων ή ανθρώπων. Σε όλες τις περιπτώσεις είναι απαραίτητη η γνώση της στάσης ενός οχήματος στο χώρο από το ίδιο το όχημα, δηλαδή της θέσης και του προσανατολισμού του, για την αυτόνομη κίνησή του στο χώρο και για την ικανοποίηση των στόχων της αποστολής του. Σε εσωτερικούς χώρους, όπου είναι αδύνατη η πρόσβαση σε δορυφορικώς μεταδιδόμενες πληροφορίες στάσης, ή σε συμφραζόμενα όπου υποδομές μέτρησης εσωτερικού χώρου απουσιάζουν, η μόνη εναλλακτική είναι η εκτίμηση του διανύσματος στάσεως, και η διαδικασία εκτίμησης αναλαμβάνεται από το ίδιο το όχημα, με βάση τους αισθητήρες που αυτό φέρει.Το αντικείμενο της παρούσας διατριβής είναι η ρομποτική αυτόνομης επίγειας κινητής βάσης σε συμφραζόμενα απουσίας μέτρησης της στάσης της. Το κυρίως περιεχόμενο της αφορά στην εκτίμηση της στάσης μίας κινητής βάσης βάσει δισδιάστατων μετρήσεων που προέρχονται από έναν αισθητήρα αποστάσεων τύπου lidar, και στην ελάττωση του σφάλματος εκτίμησης, η οποία εκτίμηση προέρχεται από παραδοσιακές μεθόδους της βιβλιογραφίας. Πιο συγκεκριμένα ερευνούμε τρόπους με τους οποίους η ευθυγράμμιση μετρήσεων από τον φυσικό αισθητήρα με εικονικές σαρώσεις—υπολογιζόμενες με βάση είτε τον χάρτη του περιβάλλοντος είτε μία δεύτερη μέτρηση—μπορεί να χρησιμοποιηθεί για την ικανοποίηση των δύο παραπάνω στόχων, και με επίδοση καλύτερη από μεθόδους της καθιερωμένης και τρέχουσας βιβλιογραφίας. Αρχικά σχεδιάζουμε μία μεθοδολογία αξιολόγησης αλγορίθμων αυτόνομης πλοήγησης, και μέσω της εφαρμογής της παρατηρούμε την ύπαρξη σφαλμάτων εκτίμησης στάσης. Στη συνέχεια ερευνούμε τρόπους μείωσης αυτών των σφαλμάτων εντός και εκτός του μηχανισμού του φίλτρου σωματιδίων. Ένας από αυτούς τους εξωτερικούς τρόπους είναι η προσθετική ευθυγράμμιση μετρήσεων με εικονικές σαρώσεις. Ανακαλύπτοντας πως η ευθυγράμμιση σαρώσεων εκτελείται στη βιβλιογραφία με τρόπο μη εύρωστο ως προς το θόρυβο μέτρησης, το μέτρο των σφαλμάτων εκτίμησης θέσης, και ρυθμιστικές παραμέτρους, αντιπροτείνουμε μεθόδους ευθυγράμμισης πραγματικών με εικονικές σαρώσεις που αντικαθιστούν τους σχετικούς παθογόνους μηχανισμούς της βιβλιογραφίας με μηχανισμούς που βασίζονται στο μετασχηματισμό Fourier και τις ιδιότητές του. Σε επίπεδο εφαρμογών σχεδιάζουμε συστήματα που δεδομένου του χάρτη του περιβάλλοντος της κινητής βάσης στοχεύουν στην εκ του μηδενός εκτίμηση της στάσης του, και στην ελάττωση του σφάλματος παρατήρησής της. Στο τέλος γενικεύουμε την προσέγγισή μας απουσία χάρτη, προκειμένου για την παραγωγή οδομετρίας με βάση μετρήσεις πανοραμικού αισθητήρα αποστάσεων τύπου lidar

Correspondenceless scan-to-map-scan matching of 2D panoramic range scans

In this article a real-time method is proposed that reduces the pose estimate error for robots capable of motion on the 2D plane. The solution that the method provides addresses the recent introduction of low-cost panoramic range scanners (2D LIDAR range sensors whose field of view is 360∘), whose use in robot localisation induces elevated pose uncertainty due to their significantly increased measurement noise compared to prior, costlier sensors. The solution employs scan-to-map-scan matching and, in contrast to prior art, its novelty lies in that matching is performed without establishing correspondences between the two input scans; rather, the matching problem is solved in closed form by virtue of exploiting the periodicity of the input signals. The correspondence-free nature of the solution allows for dispensing with the calculation of correspondences between the input range scans, which (a) becomes non-trivial and more error-prone with increasing input noise, and (b) involves the setting of parameters whose output effects are sensitive to the parameters’ correct configuration, and which does not hold universal or predictive validity. The efficacy of the proposed method is illustrated through extensive experiments on public domain data and over various measurement noise levels exhibited by the aforementioned class of sensors. Through these experiments we show that the proposed method exhibits (a) lower pose errors compared to state of the art methods, and (b) more robust pose error reduction rates compared to those which are capable of real-time execution. The source code of its implementation is available for download

Real-time 3D localization of RFID-tagged products by ground robots and drones with commercial off-the-shelf RFID equipment: challenges and solutions

Summarization: In this paper we investigate the problem of localizing passive RFID tags by ground robots and drones. We focus on autonomous robots, capable of entering a previously unknown environment, creating a 3D map of it, navigating safely in it, localizing themselves while moving, then localizing all RFID tagged objects and pinpointing their locations in the 3D map with cm accuracy. To the best of our knowledge, this is the first paper that presents the complex joint problem, including challenges from the field of robotics - i) sensors utilization, ii) local and global path planners, iii) navigation, iv) simultaneous localization of the robot and mapping - and from the field of RFIDs - vi) localization of the tags. We restrict our analysis to solutions, involving commercial UHF EPC Gen2 RFID tags, commercial off-the-self RFID readers and 3D real-time-only methods for tag-localization. We briefly present a new method, suitable for real-time 3D inventorying, and compare it with our two recent methods. Comparison is carried out on a new set of experiments, conducted in a multipath-rich indoor environment, where the actual problem is treated; i.e. our prototype robot constructs a 3D map, navigates in the environment, continuously estimates its poses as well as the locations of the surrounding tags. Localization results are given in a few seconds for 100 tags, parsing approximately 100000 measured samples from 4 antennas, collected within 4 minutes and achieving a mean 3D error of 25cm, which includes the error propagating from robotics and the uncertainty related to the "ground truth" of the tags' placement.Παρουσιάστηκε στο: 2020 IEEE International Conference on RFI

Fingerprinting localization of RFID tags with real-time performance-assessment, using a moving robot

Summarization: This work is focused on unmanned inventorying and localization, by deploying an RFID-equipped autonomous robot. The robot is able to perform Simultaneous Localization and Mapping (SLAM), thanks to its optical sensors. As the robot moves inside the target area, it continuously interrogates all RFID tags within range. Passive RFID tags, placed at known locations, are used for the estimation of the locations of the target tags, by properly manipulating the measured backscattered power. The proposed method does not depend on the location of the reader, but only on the locations of the reference tags. Hence, positioning-errors related to SLAM are not accumulated. Mobility of the robot ensures rich collection of measurements. We propose a method for dynamic, real-time configuration of the parameters of the fingerprinting algorithm and real-time evaluation of the localization error of the unknown tags. This is achieved by treating the reference tags as target tags. Thanks to this property, we further exploit mobility of the robot, repeating inventorying and localization in areas, where poor performance is initially recorded. Measurements indicate a mean error of 18cm, with standard deviation of 11cm, deploying a single antenna.Παρουσιάστηκε στο: 13th European Conference on Antennas and Propagatio