Topological map induction using neighbourhood information of places

Werner, Felix; Sitte, Joaquin; Maire, Frédéric

doi:10.1007/s10514-012-9276-1

Cited by 10 publications

(6 citation statements)

References 42 publications

(42 reference statements)

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“…Most of the maps in mobile robotics are represented as a combination of metrical and topological data structures (Werner et al 2012). For path and task planning, the representation of maps has to be simplified and adapted to the scenario where the robot has to deal with.…”

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confidence: 99%

Dynamic Bayesian network for semantic place classification in mobile robotics

2016

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In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called Dynamic Bayesian Mixture Model (DBMM), which is an improved variation of the Dynamic Bayesian Network (DBN). More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.

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confidence: 99%

Dynamic Bayesian network for semantic place classification in mobile robotics

2016

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“…117 In order to be able to recover from incorrect loop closures, Tully et al 112 introduced a multi-hypothesis approach based on a tree expansion algorithm specifically conceived for edge-ordered graphs, 32 as well as a series of pruning rules to keep the number of hypothesis under control. Finally, Werner et al 119 suggested applying stochastic local search (SLS) to produce the topological map. Finally, Werner et al 119 suggested applying stochastic local search (SLS) to produce the topological map.…”

Section: Voronoi Graphs and Neighboring Informationmentioning

confidence: 99%

“…Recently, Tao et al 107 discussed the benefits of saturated generalized Voronoi graphs (S-GVG), that employ a wall-following behavior to navigate within sensor range limits, and performed SLAM using a similar hypothesis tree. Finally, Werner et al 119 suggested applying stochastic local search (SLS) to produce the topological map.…”

Section: Voronoi Graphs and Neighboring Information Choset And Nagatmentioning

confidence: 99%

Topological simultaneous localization and mapping: a survey

2013

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One of the main challenges in robotics is navigating autonomously through large, unknown, and unstructured environments. Simultaneous localization and mapping (SLAM) is currently regarded as a viable solution for this problem. As the traditional metric approach to SLAM is experiencing computational difficulties when exploring large areas, increasing attention is being paid to topological SLAM, which is bound to provide sufficiently accurate location estimates, while being significantly less computationally demanding. This paper intends to provide an introductory overview of the most prominent techniques that have been applied to topological SLAM in terms of feature detection, map matching, and map fusion.

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“…The current modeling approaches to environment mapping by ultrasonic sensing include grid mapping [ 6 , 7 ], topological mapping [ 8 ], and geometric feature mapping [ 9 ]. Compared with other mapping approaches, feature mapping can visualize more features for secondary data processing and the mapping results are more accurate and stable.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Mapping Construction for Mobile Robot via Ultrasonic MDP and Fuzzy Model

Long

et al. 2018

Sensors

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This paper presents a modeling approach to feature classification and environment mapping for indoor mobile robotics via a rotary ultrasonic array and fuzzy modeling. To compensate for the distance error detected by the ultrasonic sensor, a novel feature extraction approach termed “minimum distance of point” (MDP) is proposed to determine the accurate distance and location of target objects. A fuzzy model is established to recognize and classify the features of objects such as flat surfaces, corner, and cylinder. An environmental map is constructed for automated robot navigation based on this fuzzy classification, combined with a cluster algorithm and least-squares fitting. Firstly, the platform of the rotary ultrasonic array is established by using four low-cost ultrasonic sensors and a motor. Fundamental measurements, such as the distance of objects at different rotary angles and with different object materials, are carried out. Secondly, the MDP feature extraction algorithm is proposed to extract precise object locations. Compared with the conventional range of constant distance (RCD) method, the MDP method can compensate for errors in feature location and feature matching. With the data clustering algorithm, a range of ultrasonic distances is attained and used as the input dataset. The fuzzy classification model—including rules regarding data fuzzification, reasoning, and defuzzification—is established to effectively recognize and classify the object feature types. Finally, accurate environment mapping of a service robot, based on MDP and fuzzy modeling of the measurements from the ultrasonic array, is demonstrated. Experimentally, our present approach can realize environment mapping for mobile robotics with the advantages of acceptable accuracy and low cost.

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Topological map induction using neighbourhood information of places

Cited by 10 publications

References 42 publications

Dynamic Bayesian network for semantic place classification in mobile robotics

Dynamic Bayesian network for semantic place classification in mobile robotics

Topological simultaneous localization and mapping: a survey

Feature Extraction and Mapping Construction for Mobile Robot via Ultrasonic MDP and Fuzzy Model

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