Significant line segments for an indoor mobile robot

Lebegue, X.; Aggarwal, J. K.

doi:10.1109/70.265923

Cited by 48 publications

(27 citation statements)

References 40 publications

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“…Kleeman and Kuc (1995) describe the use of a two-transmitter = two-receiver sonar sensor to identify planes, corners and edges within the local environment. An accuracy of §1 mm is reported with a range of up to 8 m. Lebegue and Aggarwal (1993) report the use of image processing to extract signi cant lines from an indoor scene to build up a three-dimensional model of the local environment and so locate the robot. Boudihir et al (1994) describe a system for outside location, in which dominant straight features in the environment are compared to predictions stored in memory.…”

Section: Positioning and Navigational Factors 177mentioning

confidence: 98%

Automating the construction workplace: positioning and navigational factors

Seward

2002

Construction Innovation

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The adaptation of conventional robots to construction sites is fraught with problems. Most signi cant of these are in relation to positioning, means of collision avoidance, and appropriate navigation strategy. This paper reviews the different levels of navigational autonomy that are possible and describes the system requirements for each. A taxonomy based on the concept of a Mobility Automation Level (MAL) is proposed. Each level is described and the requirements from a robot design perspective are discussed. Finally, a case study, based on an excavator with autonomously optimised movement, known as LUCIE, is used to illustrate some of the design criteria previously described and discussed.

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Section: Positioning and Navigational Factors 177mentioning

confidence: 98%

Automating the construction workplace: positioning and navigational factors

Seward

2002

Construction Innovation

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“…Fig. 6(b) shows the result of the fusion of the paired points, using (8) and (9): the estimated robot location is much more precise and the error in the location of the sensed points has been significantly reduced.…”

Section: A Fusing Range Pointsmentioning

confidence: 99%

“…This is because less information is fused in this case. Applying this process to the whole robot trajectory using (8) and (10), we obtain the result shown in Fig. 7(b).…”

Section: B Fusing Vertical Edgesmentioning

confidence: 99%

Fusing range and intensity images for mobile robot localization

Neira

Tardós

Horn

et al. 1999

IEEE Trans. Robot. Automat.

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Abstract-In this paper, we present the two-dimensional (2-D) version of the symmetries and perturbation model (SPmodel), a probabilistic representation model and an EKF integration mechanism for uncertain geometric information that is suitable for sensor fusion and integration in multisensor systems. We apply the SPmodel to the problem of location estimation in indoor mobile robotics, experimenting with the mobile robot MAC-ROBE. We have chosen two types of complementary sensory information: 1) range images; 2) intensity images; obtained from a laser sensor. Results of these experiments show that fusing simple and computationally inexpensive sensory information can allow a mobile robot to precisely locate itself. They also demonstrate the generality of the proposed fusion and integration mechanism.Index Terms-Computer vision, map-based localization, mobile robots, range finder, sensor fusion.

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“…In man made environments (indoor or outdoor between buildings, roads, ...) the main static lines have many geometrical constraints (there are many relations of parallelism and perpendicularity between horizontal lines). As in [7] these assumptions are exploited, but in our work the motion is globally computed. The vertical cue is considered in other works [10] trying to provide relevant qualitative information about the structure of the scene to recover more robustly structure and motion.…”

Section: Introductionmentioning

confidence: 99%

Motion and Structure from Significant Segments in Man Made Environments

Sagüés

Guerrero

1995

IFAC Proceedings Volumes

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An algorithm to determine the camera motion and the structure in a 3D man made environment is proposed. The algorithm is based on straight edges extracted in the image and some plentiful geometrical relations between them (verticality, horizontality, parallelism, perpendicularity). In this paper the computation of motion and structure using these constraints is tackled. The information of the tips of the straight edges are also used to determine motion. Some partial results using two images are now available. Its application to a mobile robot running in man made environments could be considered.

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Significant line segments for an indoor mobile robot

Cited by 48 publications

References 40 publications

Automating the construction workplace: positioning and navigational factors

Automating the construction workplace: positioning and navigational factors

Fusing range and intensity images for mobile robot localization

Motion and Structure from Significant Segments in Man Made Environments

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