Simultaneous landmark classification, localization and map building for an advanced sonar ring

Fazli, Saeid; Kleeman, Lindsay

doi:10.1017/s0263574706003079

Cited by 24 publications

(21 citation statements)

References 35 publications

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“…They used amplitude and range values from a SONAR sensor, whereas previous researchers had only used range values from a SONAR sensor. In a relatively recent effort of Fazli and Kleeman [11], 24 simultaneously-fired transmitters and 48 receivers were used. This SONAR configuration was tested on a vehicle in motion.…”

Section: Indexmentioning

confidence: 99%

Algorithm Fusion for Feature Extraction and Map Construction From SONAR Data

Ismail¹,

Balachandran²

2015

IEEE Sensors J.

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Feature extraction is an important aspect of simultaneous localization and mapping, a process, in which a mobile platform is used to create a map of an unknown environment, while simultaneously locating the platform's own position within the constructed map or environment. Geometric shapes or features, such as lines, circles, and interior and exterior corners, are determined as a part of this process, and these features may be used as landmarks. In this paper, an original feature extraction algorithm specific to distance measurements obtained through SONAR sensor data is presented. This algorithm has been put together by combining the SONAR salient feature extraction algorithm and the triangulation Hough-based fusion with the point-in-polygon detection. The reconstructed maps obtained through simulations and experimental data with the fusion algorithm are compared with the maps obtained with existing feature extraction algorithms. Based on the results, it is suggested that the proposed algorithm can be considered as an option for the data obtained from SONAR sensors in environments, where the other forms of sensing are not viable.

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Section: Indexmentioning

confidence: 99%

Algorithm Fusion for Feature Extraction and Map Construction From SONAR Data

Ismail¹,

Balachandran²

2015

IEEE Sensors J.

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“…The past literature on wall-following robots is vast, however we can immediately distinguish this work from potential-field approaches [17], [18], which need a priori knowledge about the environment, as well as from approaches based on mapping [19], [20], which require more sophisticated sensors than assumed here and need relatively high computational power and memory. We want to restrict attention to the so-called "reactive" or "feedback" [21] paradigm of robot control, where the task is specified as a dynamical relation instead of a prescribed plan.…”

Section: A Brief Survey Of Prior Literaturementioning

confidence: 99%

Toward dynamical sensor management for reactive wall-following

Koditschek

2013

2013 IEEE International Conference on Robotics and Automation

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We propose a new paradigm for reactive wallfollowing by a planar robot taking the form of an actively steered sensor model that augments the robot's motion dynamics. We postulate a foveated sensor capable of delivering third-order infinitesimal (range, tangent, and curvature) data at a point along a wall (modeled as an unknown smooth plane curve) specified by the angle of the ray from the robot's body that first intersects it. We develop feedback policies for the coupled (point or unicycle) sensorimotor system that drive the sensor's foveal angle as a function of the instantaneous infinitesimal data, in accord with the trade-off between a desired standoff and progress-rate as the wall's curvature varies unpredictably in the manner of an unmodeled noise signal. We prove that in any neighborhood within which the thirdorder infinitesimal data accurately predicts the local "shape" of the wall, neither robot will ever hit it. We empirically demonstrate with comparative physical studies that the new active sensor management strategy yields superior average tracking performance and avoids catastrophic collisions or wall losses relative to the passive sensor variant. Abstract-We propose a new paradigm for reactive wallfollowing by a planar robot taking the form of an actively steered sensor model that augments the robot's motion dynamics. We postulate a foveated sensor capable of delivering third-order infinitesimal (range, tangent, and curvature) data at a point along a wall (modeled as an unknown smooth plane curve) specified by the angle of the ray from the robot's body that first intersects it. We develop feedback policies for the coupled (point or unicycle) sensorimotor system that drive the sensor's foveal angle as a function of the instantaneous infinitesimal data, in accord with the trade-off between a desired standoff and progress-rate as the wall's curvature varies unpredictably in the manner of an unmodeled noise signal. We prove that in any neighborhood within which the thirdorder infinitesimal data accurately predicts the local "shape" of the wall, neither robot will ever hit it. We empirically demonstrate with comparative physical studies that the new active sensor management strategy yields superior average tracking performance and avoids catastrophic collisions or wall losses relative to the passive sensor variant.

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“…Depending on the kind of sensors used, different approaches are taken, for example: in the case of sonar-sensors [18,53], in the case of laser rangefinder sensors [37,36], in the case of video sensors [44,17], or combinations of them as in [33,7].…”

Section: Related Workmentioning

confidence: 99%

Approximate robotic mapping from sonar data by modeling perceptions with antonyms

Guadarrama

Ruiz-Mayor

2010

Information Sciences

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a b s t r a c tThis work, inspired by the idea of ''Computing with Words and Perceptions" proposed by Zadeh in [57,59], focuses on how to transform measurements into perceptions [22] for the problem of map building by Autonomous Mobile Robots. We propose to model the perceptions obtained from sonar-sensors as two grid maps: one for obstacles and another for empty-spaces. The rules used to build and integrate these maps are expressed by linguistic descriptions and modeled by fuzzy rules. The main difference of this approach from other studies reported in the literature is that the method presented here is based on the hypothesis that the concepts ''occupied" and ''empty" are antonyms rather than complementary (as it happens in probabilistic approaches), or independent (as it happens in the previous fuzzy models).Controlled experimentation with a real robot in three representative indoor environments has been performed and the results presented. We offer a qualitative and quantitative comparison of the estimated maps obtained by the probabilistic approach, the previous fuzzy method and the new antonyms-based fuzzy approach. It is shown that the maps obtained with the antonyms-based approach are better defined, capture better the shape of the walls and of the empty-spaces, and contain less errors due to rebounds and short-echoes. Furthermore, in spite of noise and low resolution inherent to the sonar-sensors used, the maps obtained are accurate and tolerant to imprecision.

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Simultaneous landmark classification, localization and map building for an advanced sonar ring

Cited by 24 publications

References 35 publications

Algorithm Fusion for Feature Extraction and Map Construction From SONAR Data

Algorithm Fusion for Feature Extraction and Map Construction From SONAR Data

Toward dynamical sensor management for reactive wall-following

Approximate robotic mapping from sonar data by modeling perceptions with antonyms

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