Robust environment mapping using flux skeletons

Rezanejad, Morteza; Samari, Babak; Rekleitis, Ioannis; Siddiqi, Kaleem; Dudek, Gregory

doi:10.1109/iros.2015.7354186

Cited by 12 publications

(10 citation statements)

References 25 publications

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“…where (p, γF ) is the concatenation of the 2D coordinate values p = (x, y) T with the K feature values F = (f (1) , ..., f (K) ) T . γ is a K × K diagonal matrix which contains the K weights controlling the influence of each feature.…”

Section: Topology Matchingmentioning

confidence: 99%

“…While such approaches are well known for their theoretical elegance, computing such representations in practice is complicated when the data is sparse and noisy. In this article, we first present the online construction of a topological map and the implementation of a control law for guiding the robot to the nearest unexplored area, first presented in [1]. The proposed method operates by allowing the robot to localize itself on a partially constructed map, calculate a path to unexplored parts of the environment (frontiers), compute a robust terminating condition when the robot has fully explored the environment, and achieve loop closure detection.…”

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confidence: 99%

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Average Outward Flux Skeletons for Environment Mapping and Topology Matching

Rezanejad¹,

Samari²,

Karimi³

et al. 2021

Preprint

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We consider how to directly extract a road map (also known as a topological representation) of an initially-unknown 2-dimensional environment via an online procedure that robustly computes a retraction of its boundaries. While such approaches are well known for their theoretical elegance, computing such representations in practice is complicated when the data is sparse and noisy. In this article, we first present the online construction of a topological map and the implementation of a control law for guiding the robot to the nearest unexplored area, first presented in [1]. The proposed method operates by allowing the robot to localize itself on a partially constructed map, calculate a path to unexplored parts of the environment (frontiers), compute a robust terminating condition when the robot has fully explored the environment, and achieve loop closure detection. The proposed algorithm results in smooth safe paths for the robot's navigation needs. The presented approach is an any time algorithm that has the advantage that it allows for the active creation of topological maps from laser scan data, as it is being acquired. The resulting map is stable under variations to noise and the initial conditions. We also propose a navigation strategy based on a heuristic where the robot is directed towards nodes in the topological map that open to empty space. The method is evaluated on both synthetic data and in the context of active exploration using a Turtlebot 2. Our results demonstrate a complete mapping of different environments with smooth topological abstraction without spurious edges. We then extend the work in [1] by presenting a topology matching algorithm that leverages the strengths of a particular spectral correspondence method, FOCUSR [2], to match the mapped environments generated from our topology making algorithm. Here, we concentrated on implementing a system that could be used to match the topologies of the mapped environment by using AOF Skeletons. In topology matching between two given maps and their AOF skeletons, we first find correspondences between points on the AOF skeletons of two different environments. We then align the (2D) points of the environments themselves. We also compute a distance measure between two given environments, based on their extracted AOF skeletons and their topology, as the sum of the matching errors between corresponding points. We evaluate our topology matching algorithm and demonstrate promising results on a few environments of increasing complexity, with simulated sensor noise.

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Section: Topology Matchingmentioning

confidence: 99%

mentioning

confidence: 99%

Average Outward Flux Skeletons for Environment Mapping and Topology Matching

Rezanejad¹,

Samari²,

Karimi³

et al. 2021

Preprint

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“…One can query a path efficiently and completely on the graph during the exploration. Rezanejad et al [8] suggest to use the topological map generated by the flux skeleton based method on the occupancy grid map, where the skeleton is generated online by utilizing image-based method instead of constructing incrementally with the exploration.…”

Section: A Map Representationmentioning

confidence: 99%

Efficient Autonomous Robotic Exploration With Semantic Road Map in Indoor Environments

Wang

Zhu

Teng

et al. 2019

IEEE Robot. Autom. Lett.

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In this paper, we propose an integrated framework for the autonomous robotic exploration in indoor environments. Specially, we present a hybrid map, named Semantic Road Map (SRM), to represent the topological structure of the explored environment and facilitate decision-making in the exploration. The SRM is built incrementally along with the exploration process. It is a graph structure with collision-free nodes and edges that are generated within the sensor coverage. Moreover, each node has a semantic label and the expected information gain at that location. Based on the concise SRM, we present a novel and effective decision-making model to determine the next-best-target (NBT) during the exploration. The model concerns the semantic information, the information gain, and the path cost to the target location. We use the nodes of SRM to represent the candidate targets, which enables the target evaluation to be performed directly on the SRM. With the SRM, both the information gain of a node and the path cost to the node can be obtained efficiently. Besides, we adopt the cross-entropy method to optimize the path to make it more informative. We conduct experimental studies in both simulated and real-world environments, which demonstrate the effectiveness of the proposed method.

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“…In this work, we selected the flux skeleton approach since this medial representation is robust to noise in the shape boundary. Flux skeletons were introduced by Dimitrov et al (2003) and have been improved in different applications (Rezanejad & Siddiqi (2013), Rezanejad et al (2015)). In our implementation we used the package developed by Rezanejad et al (2015).…”

Section: Computing the Medial Axismentioning

confidence: 99%

“…Flux skeletons were introduced by Dimitrov et al (2003) and have been improved in different applications (Rezanejad & Siddiqi (2013), Rezanejad et al (2015)). In our implementation we used the package developed by Rezanejad et al (2015). We will review the geometry of flux skeletons in the following.…”

Section: Computing the Medial Axismentioning

confidence: 99%

Fully-automated tongue detection in ultrasound images

Karimi

Ménard

Laporte

2018

The Journal of the Acoustical Society of America

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during my academic years at ÉTS and guided me with every difficulties I faced doing my masters. She has been an exceptional mentor and supervisor to me and I am really thankful for having such a great opportunity to be trained by her. I am deeply thankful to Professor Lucie Ménard who agreed to co-supervise me for my masters studies and helped in different aspects. She provided me with the data I needed to complete this project and I always benefited from her thoughtful comments. I also have to thank Natural Sciences and Engineering Research Council (NSERC) and Fonds de recherche du Québec-Nature et technologies (FRQNT) for their financial support. Also, I would like to thank my lab-mates at LATIS at École de technologie supérieure, and specially, Jawad Dahmani, Arnaud Brignol, Nassim Guerroumi, and Anh-Thu Gloria Hang-Vo. Besides, I am grateful to Professor Kaleem Siddiqi from McGill university for his support while I was a student member of CREATE-MIA program. Finally, I would like to conclude by expressing my deep love toward my family, and by thanking them for their unconditional support. My greatest gratitudes are to my family: Morteza, my husband, who stood by my side all the time during my graduate studies and supporting me every step in the way. My mother, and father, and my greater sisters for their unconditional love and support. Détection Entièrement Automatisée de la Langue dans les Images Ultrasonores Elham KARIMI RÉSUMÉ Le suivi de la langue dans les images échographiques fournit des informations sur sa forme et sa cinématique pendant la parole. Dans ce mémoire, nous proposons des solutions d'ingénierie pour mieux exploiter les cadres existants et les déployer afin de convertir un système de suivi semi-automatique du contour de la langue en un système entièrement automatique. Les méthodes actuelles de détection / suivi de la langue nécessitent une initialisation manuelle ou un entraînement utilisant de grandes quantités d'images étiquetées. Ce mémoire présente une nouvelle méthode d'extraction des contours de la langue dans les images échographiques, qui ne nécessite aucun entraînement ni intervention manuelle. Le procédé consiste à: (1) appliquer un filtre de symétrie de phase pour mettre en évidence des régions contenant éventuellement le contour de la langue; (2) appliquer un seuillage adaptatif et classer les niveaux de gris pour sélectionner des régions qui incluent le contour de la langue ou se trouvent à proximité de ce dernier; (3) la squelettisation de ces régions pour extraire une courbe proche du contour de la langue et (4) l'initialisation d'un contour actif précis à partir de cette courbe. Deux nouvelles mesures de qualité ont également été développées pour prédire la fiabilité de la méthode, de sorte que des trames optimales puissent être choisies pour initialiser en toute confiance un suivi de la langue entièrement automatisé. Ceci est réalisé en générant et en choisissant automatiquement un ensemble de points pouvant remplacer les points segmentés manuellement pour une approche de su...

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Robust environment mapping using flux skeletons

Cited by 12 publications

References 25 publications

Average Outward Flux Skeletons for Environment Mapping and Topology Matching

Average Outward Flux Skeletons for Environment Mapping and Topology Matching

Efficient Autonomous Robotic Exploration With Semantic Road Map in Indoor Environments

Fully-automated tongue detection in ultrasound images

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