PRONTO: a system for mobile robot navigation via CAD-model guidance

Γαστεράτος, Αντώνιος; Beltrán, Carlos E.; Metta, Giorgio; Sandini, Giulio

doi:10.1016/s0141-9331(01)00142-9

Cited by 13 publications

(7 citation statements)

References 13 publications

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“…The most common active stereo vision systems comprise a pair cameras horizontally aligned (Gasteratos et al (2002)), (Samson et al (2006)). In these systems the movement of the stereo rig is done by means of 2 degrees of freedom: one for the horizontal movement (pan) and the other for the vertical one (tilt); moreover each of the cameras obeys to an independent pan movement (vergence), which raises the total degrees of freedom of the system to four.…”

Section: Active Robot Visionmentioning

confidence: 99%

Image Stabilization in Active Robot Vision

Amanatiadis¹,

Γαστεράτος²,

Papadakis³

et al. 2010

Robot Vision

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Section: Active Robot Visionmentioning

confidence: 99%

Image Stabilization in Active Robot Vision

Amanatiadis¹,

Γαστεράτος²,

Papadakis³

et al. 2010

Robot Vision

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“…The head. The head has been utilized as an accurate measuring device for the purpose of ROVISION project [11,12]. More specifically, it has been mounted on a walking/climbing and the measurements obtained by it have been used for self-localization of the robot.…”

Section: The Stereo Headmentioning

confidence: 99%

Factors Affecting the Accuracy of an Active Vision Head

Γαστεράτος

Sandini

2002

Methods and Applications of Artificial Intelligence

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Abstract. In any measuring system the categorization of the error generation factors leads to simplification of complex error problems and to higher suppression of the error. In this paper we categorize, quantify and analyze the errors that affect a binocular active vision head. Simulations have been made and experimental results on a high resolution pan-tilt-vergence mechanism are also proposed. As a conclusion it can be said that the system performs optimal when it is initialized so that the two cameras are perfectly aligned and perpendicular to the baseline. Small variations in the vergence angle or small horizontal deviations of the principal point alters the measurement dramatically. On the other hand, variations in pan and tilt and vertical deviations of the principal point, affect the measurement insignificantly.

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“…When the map is obtained by other means, the exploration stage can be avoided. Such is the case in [9] and [10], where the system compares the visual information acquired during navigation, with a Computer-Aided Design (CAD) model.…”

Section: Introductionmentioning

confidence: 99%

Photometric Path Planning for Vision-Based Navigation

Martinez

Caron

Pégard

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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We present a vision-based navigation system that uses a visual memory to navigate. Such memory corresponds to a topological map of key images created from moving a virtual camera over a model of the real scene. The advantage of our approach is that it provides a useful insight into the navigability of a visual path without relying on a traditional learning stage. During the navigation stage, the robot is controlled by sequentially comparing the images stored in the memory with the images acquired by the onboard camera. The evaluation is conducted on a robotic arm equipped with a camera and the model of the environment corresponds to a top view image of an urban scene.

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PRONTO: a system for mobile robot navigation via CAD-model guidance

Cited by 13 publications

References 13 publications

Image Stabilization in Active Robot Vision

Image Stabilization in Active Robot Vision

Factors Affecting the Accuracy of an Active Vision Head

Photometric Path Planning for Vision-Based Navigation

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