scholarly journals Multi-Objective Optimization of Loop Closure Detection Parameters for Indoor 2D Simultaneous Localization and Mapping

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1906
Author(s):  
Dongxiao Han ◽  
Yuwen Li ◽  
Tao Song ◽  
Zhenyang Liu
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Ground Truth ◽  
Optimization Method ◽  
Evaluation Metrics ◽  
Multi Objective Optimization ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Multi Objective ◽  
Evaluation Approach ◽  
Localization And Mapping

Aiming at addressing the issues related to the tuning of loop closure detection parameters for indoor 2D graph-based simultaneous localization and mapping (SLAM), this article proposes a multi-objective optimization method for these parameters. The proposed method unifies the Karto SLAM algorithm, an efficient evaluation approach for map quality with three quantitative metrics, and a multi-objective optimization algorithm. More particularly, the evaluation metrics, i.e., the proportion of occupied grids, the number of corners and the amount of enclosed areas, can reflect the errors such as overlaps, blurring and misalignment when mapping nested loops, even in the absence of ground truth. The proposed method has been implemented and validated by testing on four datasets and two real-world environments. For all these tests, the map quality can be improved using the proposed method. Only loop closure detection parameters have been considered in this article, but the proposed evaluation metrics and optimization method have potential applications in the automatic tuning of other SLAM parameters to improve the map quality.

Online appearance-based place recognition and mapping algorithms for autonomous robot navigation

2021 ◽  
Author(s):  
Κωνσταντίνος Τσιντώτας
Keyword(s):  
Image Processing ◽  
Robot Navigation ◽  
Simultaneous Localization And Mapping ◽  
Autonomous Robot ◽  
Place Recognition ◽  
Mapping Algorithms ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Localization And Mapping ◽  
Autonomous Robot Navigation

Τα αυτόνομα ρομποτικά συστήματα αποτελούν ένα από τα τμήματα που βρίσκονται στην αιχμή της τεχνολογίας και της έρευνας. Τα σύγχρονα ρομποτικά συστήματα που δημιουργούνται δύναται να έχουν την ικανότητα να μπορούν να προσδιορίσουν την θέση τους μέσα στο περιβάλλον το οποίο περιπλανιούνται. Η επίτευξη του συγκεκριμένου στόχου επιτυγχάνεται μέσω της κατασκευής ενός χάρτη την στιγμή που πλοηγείται, μέσω ενός μηχανισμού που είναι ευρέως γνωστός ως ταυτόχρονος προσδιορισμός θέσης και τοποθεσίας (Simultaneous Localization and Mapping –SLAM). Ο χάρτης δημιουργείται μέσω των μετρήσεων που δέχεται το σύστημα από τους επιμέρους αισθητήρες που είναι τοποθετημένοι επάνω στο ρομποτικό σύστημα. Η αύξηση της υπολογιστικής ισχύς τα τελευταία χρόνια και η ευρέως διαδεδομένη χρήση των καμερών οδήγησε στα αυτόνομα συστήματα να επιλέγεται ως κύρια πηγή απόκτησης δεδομένων η χρήση καμερών. Καθώς όμως το ρομπότ διασχίζει την τροχιά του μέσα σε ένα άγνωστο περιβάλλον είναι έντονος ο κίνδυνος δημιουργίας ενός χάρτη με εσφαλμένα στοιχεία στην σχεδιασμένη τροχιά, με κύρια αίτια που μπορεί να σχετίζονται στην κακή εκτίμηση μετρήσεων των αισθητήριων ή σε ενδεχόμενη δυσλειτουργία των ενσωματωμένων οργάνων του συστήματος. Το πρόβλημα που αναφέρθηκε αποτελεί κίνδυνο για την περάτωση της αποστολής του ρομποτικού συστήματος, το οποίο όμως μπορεί να διορθωθεί με επαναπροσδιορισμό της τροχιάς του εφόσον είναι εφικτή η αναγνώριση της περιοχής που διασχίζει μέσω των οπτικών αισθητηρίων που διαθέτει. Τα συστήματα τα οποία βασίζονται αποκλειστικά στις κάμερες ως μέσα αναγνώρισης περιοχής είναι γνωστά ως εμφάνισης βασιζόμενα συστήματα (appearance based systems) και οι τεχνικές που προσπαθούν να λύσουν το πρόβλημα είναι γνωστές ως ανίχνευση κλειστών βρόγχων (loop closure detection). Η παρούσα διδακτορική διατριβή εστιάζει στην δημιουργία ενός αντίστοιχου συστήματος ικανού να αναγνωρίσει μια επαναλαμβανόμενα επισκεπτόμενη περιοχή μέσω των εισερχόμενων εικόνων που δέχεται. Η επίτευξη του συστήματος στηρίζεται σε τρείς σημαντικές μονάδες. Στην αρχή έρχεται η μονάδα επεξεργασίας εικόνας (image processing) με την οποία είναι εφικτή η ερμηνεία των εισερχόμενων εικόνων. Μέσω τεχνικών περιγραφής εικόνας είναι δυνατή η λήψη σημείων ενδιαφέροντος ικανά να περιγράψουν την σκηνή που αντιμετωπίζει το ρομπότ. Κάθε εικόνα ενδέχεται να περιέχει εκατοντάδες τοπικά σημεία ενδιαφέροντος με αποτέλεσμα να μπορεί να περιγραφεί ως ένα σύνολο από επιμέρους χαρακτηριστικά. Ο χάρτης (map) αποτελεί την μονάδα που είναι υπεύθυνη για την αναπαράσταση του κόσμου που γνωρίζει το ρομπότ με βάση τα χαρακτηριστικά που έχουν ληφθεί από τις σκηνές που έχει επισκεφθεί. Τέλος, έρχεται η μονάδα πεποίθησης πιθανής περιοχής (belief generator), η οποία συνδυάζει τα εισερχόμενα δεδομένα με τα δεδομένα του χάρτη ώστε να είναι σε θέση να λάβει την απόφαση για το αν το σύστημα βρίσκεται ή όχι σε οικεία περιοχή με σκοπό να επαναπροσδιορίσει την τροχιά του.

scholarly journals Affine-Invariant Geometric Constraints-Based High Accuracy Simultaneous Localization and Mapping

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Gangchen Hua ◽  
Xu Tan
Keyword(s):  
Large Scale ◽  
Simultaneous Localization And Mapping ◽  
Geometric Constraints ◽  
Long Term Memory ◽  
Affine Invariant ◽  
Loop Closure ◽  
Visual Words ◽  
Loop Closure Detection ◽  
Localization And Mapping

In this study we describe a new appearance-based loop-closure detection method for online incremental simultaneous localization and mapping (SLAM) using affine-invariant-based geometric constraints. Unlike other pure bag-of-words-based approaches, our proposed method uses geometric constraints as a supplement to improve accuracy. By establishing an affine-invariant hypothesis, the proposed method excludes incorrect visual words and calculates the dispersion of correctly matched visual words to improve the accuracy of the likelihood calculation. In addition, camera’s intrinsic parameters and distortion coefficients are adequate for this method. 3D measuring is not necessary. We use the mechanism of Long-Term Memory and Working Memory (WM) to manage the memory. Only a limited size of the WM is used for loop-closure detection; therefore the proposed method is suitable for large-scale real-time SLAM. We tested our method using the CityCenter and Lip6Indoor datasets. Our proposed method results can effectively correct the typical false-positive localization of previous methods, thus gaining better recall ratios and better precision.

Student Admission Assesment using Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA)

2018 ◽  
Author(s):  
Rivalri Kristianto Hondro ◽  
Mesran Mesran ◽  
Andysah Putera Utama Siahaan
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Selection Process ◽  
Initial Step ◽  
Optimization Method ◽  
Ratio Analysis ◽  
Multi Objective Optimization ◽  
Prospective Students ◽  
Multi Objective

Procurement selection process in the acceptance of prospective students is an initial step undertaken by private universities to attract superior students. However, sometimes this selection process is just a procedural process that is commonly done by universities without grouping prospective students from superior students into a class that is superior compared to other classes. To process the selection results can be done using the help of computer systems, known as decision support systems. To produce a better, accurate and objective decision result is used a method that can be applied in decision support systems. Multi-Objective Optimization Method by Ratio Analysis (MOORA) is one of the MADM methods that can perform calculations on the value of criteria of attributes (prospective students) that helps decision makers to produce the right decision in the form of students who enter into the category of prospective students superior.

Multi-objective optimization of optimal capacitor allocation in radial distribution systems

2020 ◽  
Vol 21 (3) ◽  
Author(s):  
Sayed Mir Shah Danish ◽  
Mikaeel Ahmadi ◽  
Atsushi Yona ◽  
Tomonobu Senjyu ◽  
Narayanan Krishna ◽  
...  
Keyword(s):  
Radial Distribution ◽  
Distribution System ◽  
Distribution Systems ◽  
Reactive Power ◽  
Stability Index ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Size ◽  
Multi Objective Optimization ◽  
Multi Objective

AbstractThe optimal size and location of the compensator in the distribution system play a significant role in minimizing the energy loss and the cost of reactive power compensation. This article introduces an efficient heuristic-based approach to assign static shunt capacitors along radial distribution networks using multi-objective optimization method. A new objective function different from literature is adapted to enhance the overall system voltage stability index, minimize power loss, and to achieve maximum net yearly savings. However, the capacitor sizes are assumed as discrete known variables, which are to be placed on the buses such that it reduces the losses of the distribution system to a minimum. Load sensitive factor (LSF) has been used to predict the most effective buses as the best place for installing compensator devices. IEEE 34-bus and 118-bus test distribution systems are utilized to validate and demonstrate the applicability of the proposed method. The simulation results obtained are compared with previous methods reported in the literature and found to be encouraging.

scholarly journals A Multi-Objective Optimization Method for Hospital Admission Problem—A Case Study on Covid-19 Patients

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 38
Author(s):  
Amr Mohamed AbdelAziz ◽  
Louai Alarabi ◽  
Saleh Basalamah ◽  
Abdeltawab Hendawi
Keyword(s):  
Medical Care ◽  
Real Life ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Admission Process ◽  
Multi Objective ◽  
Admission Time ◽  
King Faisal Specialist Hospital ◽  
Number Of Patients ◽  
Massive Number

The wide spread of Covid-19 has led to infecting a huge number of patients, simultaneously. This resulted in a massive number of requests for medical care, at the same time. During the first wave of Covid-19, many people were not able to get admitted to appropriate hospitals because of the immense number of patients. Admitting patients to suitable hospitals can decrease the in-bed time of patients, which can lead to saving many lives. Also, optimizing the admission process can minimize the waiting time for medical care, which can save the lives of severe cases. The admission process needs to consider two main criteria: the admission time and the readiness of the hospital that will accept the patients. These two objectives convert the admission problem into a Multi-Objective Problem (MOP). Pareto Optimization (PO) is a common multi-objective optimization method that has been applied to different MOPs and showed its ability to solve them. In this paper, a PO-based algorithm is proposed to deal with admitting Covid-19 patients to hospitals. The method uses PO to vary among hospitals to choose the most suitable hospital for the patient with the least admission time. The method also considers patients with severe cases by admitting them to hospitals with the least admission time regardless of their readiness. The method has been tested over a real-life dataset that consisted of 254 patients obtained from King Faisal specialist hospital in Saudi Arabia. The method was compared with the lexicographic multi-objective optimization method regarding admission time and accuracy. The proposed method showed its superiority over the lexicographic method regarding the two criteria, which makes it a good candidate for real-life admission systems.

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