Echolocation — From range to outline segments

McKerrow, P.J.

doi:10.1016/0921-8890(93)90025-8

Cited by 40 publications

(17 citation statements)

References 3 publications

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“…A classification standard for indoor target types emerging is that of planes, corners and edges (Bozma and Kuc 1991a, Barshan and Kuc 1990, Peremans et al 1993, Sabatini 1992, Leonard and Durrant-Whyte 1991, McKerrow 1993. The sensor approach presented here is novel in the sense that it classifies all three target types with the one stationary sensor, simultaneously in some cases, with high accuracy and discrimination.…”

Section: Introductionmentioning

confidence: 99%

“…The sensor approach presented here is novel in the sense that it classifies all three target types with the one stationary sensor, simultaneously in some cases, with high accuracy and discrimination. Our approach has higher speed and accuracy, particularly in bearing, compared to single transducer systems that rely on multiple displaced readings and wheel odometery for target classification (Bozma and Kuc 1991, McKerrow 1993, Leonard and Durrant-Whyte 1991. Sonar sensors have been reported previously that can classify two of the three target types -- Barshan and Kuc (1990) discriminate planes and corners based on pulse amplitude measurements; and Peremans et al (1993) uses time of flight (TOF) to classify planes and edges and employs sensor movement to distinguish corners and planes.…”

Section: Introductionmentioning

confidence: 99%

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Mobile Robot Sonar for Target Localization and Classification

Kleeman

Kuc

1995

The International Journal of Robotics Research

251

188

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A novel sonar array is presented that has applications in mobile robotics for localization and mapping of indoor environments. The ultrasonic sensor localizes and classifies multiple targets in two dimensions to ranges of up to 8 meters. By accounting for effects of temperature and humidity, the system is accurate to within a millimeter and 0.1 degrees in still air. Targets separated by 10 mm in range can be discriminated. The error covariance matrix for these measurements is derived to allow fusion with other sensors. Targets are statistically classified into four reflector types: planes, corners, edges and unknown.The paper establishes that two transmitters and two receivers are necessary and sufficient to distinguish planes, corners and edges. A sensor array is presented with this minimum number of transmitters and receivers. A novel design approach is that the receivers are closely spaced so as to minimize the correspondence problem of associating different receiver echoes from multiple targets.A linear filter model for pulse transmission, reception, air absorption and dispersion is used to generate a set of templates for the echo as a function of range and bearing angle. The optimal echo arrival time is estimated from the maximum cross-correlation of the echo with the templates. The use of templates also allows overlapping echoes and disturbances to be rejected. Noise characteristics are modeled for use in the maximum likelihood estimates of target range and bearing. Experimental results are presented to verify assumptions and characterize the sensor.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mobile Robot Sonar for Target Localization and Classification

Kleeman

Kuc

1995

The International Journal of Robotics Research

251

188

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“…위치 불확실성 문제 완화를 위해 대부분 상당히 많은 개수의 빔 폭이 협소한 고지향성 초음파 센서 를 사용하여 왔으나 [3][4][5][6][7][8][9][10][11][12] 그림2. 빔폭중첩으로인한위치불확실성감소.…”

Section: 자율 주행 이동로봇의 환경 지도 작성 및 장애물 탐지/ 회피를 위해서 주로 사용되는 센서 종류로는 카메라 unclassified

Optimal Design of Overlapped Ultrasonic Sensor Ring for High Resolution Obstacle Detection

Kim¹,

Kim²

2011

Journal of Institute of Control, Robotics and Systems

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This paper presents the optimal design of an overlapped ultrasonic sensor ring for high resolution obstacle detection of an autonomous mobile robot. It is assumed that a set of low directivity ultrasonic sensors of the same type are arranged along a circle of nonzero radius at a regular spacing with their beams overlapped. First, taking into account the dead angle region, the entire range of obstacle detection is determined with reference to the center of an overlapped ultrasonic sensor ring. Second, the optimal design index of an overlapped ultrasonic sensor ring is defined as the area closeness of three sensing subzones resulting from beam overlap. Third, the lower and upper bounds on the number of ultrasonic sensors are derived, which can guarantee minimal beam overlap and also avoid excessive beam overlap among adjacent ultrasonic sensors. Fourth, employing a commercial low directivity ultrasonic sensor, an optimal design example of an overlapped ultrasonic sensor ring is given along with the ultrasonic sensor ring prototype mounted on top of a mobile robot. Finally, some experimental results using our prototype ultrasonic sensor ring are given to demonstrate the validity and performance of an optimally overlapped ultrasonic sensor ring for high resolution obstacle detection.

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“…This can be due to a variety of reasons: occlusion between objects, the size of the object in relation to the sensor field of view, an inappropriate feature model, or just because the nature of the sensor makes the estimation of the landmark location impossible from only one vantage point (e.g. wide-beam sonar; Leonard J. et al 2002, McKerrow P. 1993. Estimating partially observable features has been an important research topic in computer vision using stereo vision and bearing only information, where the initialisation of the feature position is a significant problem.…”

Section: Denseslam: Applicationsmentioning

confidence: 99%

“…Consistent estimation is achieved by adding the past vehicle positions to the state vector and combining the observations from multiple points of view until there is enough information to validate a feature. In McKerrow (1993), intersection of constant depth range of ultrasonic sensors is used to determine the location of features from multiple vantage points. Having a comprehensive representation of the environment will enable a delayed processing to determine whether part of the map can qualify as a landmark.…”

Section: Denseslam: Applicationsmentioning

confidence: 99%