A strategy for acquiring an environmental model with panoramic sensing by a mobile robot

Ishiguro, Hiroshi; Maeda, Takao; Miyashita, Takahiro; Tsuji, Saburo

doi:10.1109/robot.1994.351401

Cited by 18 publications

(6 citation statements)

References 10 publications

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“…Indeed, a line in space will correspond to a cosine arc in a panoramic image, whose curvature is inversely proportional to the distance between the sensor and the line. This characteristic has already been used in robotics 4,28 and recently in calibration. 29 We took it as a starting point to develop the proposed method of active calibration.…”

Section: Active Calibrationmentioning

confidence: 99%

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Multispectral omnidirectional vision sensor: design, calibration, and utilization

Romain¹,

Thomas

Garda³

2007

Opt. Eng

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The 3-D reconstruction of an environment with high accuracy and real textures represents an important challenge for applications related to multimedia and cinematography. The complete development, from electronic design to algorithmic restitution of the environment, of a multispectral omnidirectional stereoscopic sensor that tackles this problem is presented. Although panoramic sensors exist, the vision system presented here has four channels of acquisition coupled to a projector of structured light emitting in the infrared. This original process separates the acquisition of color images from 3-D reconstruction. The panoramic color images are corrected using the International Color Consortium profile applied to images coming from the two cameras. The reconstruction of the environment uses only the information available in the infrared channel. An active calibration technique is also presented. Experimental results demonstrate the feasibility of this new class of multispectral omnidirectional sensors.

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Section: Active Calibrationmentioning

confidence: 99%

“…Initially developed for mobile robotics [1][2][3][4] and remote monitoring, 5,6 the systems are now used in multimedia application. The rise of panoramic vision in this field corresponds to the development of tools allowing full environment simulation, visualization, and integration with QuickTime VR technology.…”

Section: Introductionmentioning

confidence: 99%

Multispectral omnidirectional vision sensor: design, calibration, and utilization

Romain¹,

Thomas

Garda³

2007

Opt. Eng

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“…The idea of using omnidirectional camera system for reconstructing environments from video imagery has been explored by Yagi et al [24] and Ishiguro et al [8], [7], [9]. These authors presented an omnidirectional camera system based on a conical mirror and described how the measurements obtained from the video imagery acquired with their camera system could be combined with odometry measurements from a robot platform to construct maps of the robots environment.…”

Section: Related Workmentioning

confidence: 99%

VideoPlus: a method for capturing the structure and appearance of immersive environments

Taylor

2002

IEEE Trans. Visual. Comput. Graphics

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This paper presents a simple approach to capturing the appearance and structure of immersive scenes based on the imagery acquired with an omnidirectional video camera. The scheme proceeds by combining techniques from structure-from-motion with ideas from image-based rendering. An interactive photogrammetric modeling scheme is used to recover the locations of a set of salient features in the scene (points and lines) from image measurements in a small set of keyframe images. The estimates obtained from this process are then used as a basis for estimating the position and orientation of the camera at every frame in the video clip. By augmenting the video sequence with pose information, we provide the end-user with the ability to index the video sequence spatially as opposed to temporally. This allows the user to explore the immersive scene by interactively selecting the desired viewpoint and viewing direction. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. AbstractÐThis paper presents a simple approach to capturing the appearance and structure of immersive scenes based on the imagery acquired with an omnidirectional video camera. The scheme proceeds by combining techniques from structure from motion with ideas from image based rendering. An interactive photogrammetric modeling scheme is used to recover the locations of a set of salient features in the scene (points and lines) from image measurements in a small set of keyframe images. The estimates obtained from this process are then used as a basis for estimating the position and orientation of the camera at every frame in the video clip. By augmenting the video sequence with pose information we provide the end user with the ability to index the video sequence spatially as opposed to temporally. This allows the user to explore the immersive scene by interactively selecting the desired viewpoint and viewing direction.

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“…They also gave algorithms to build snapshot models with planar faces from range data, and to perform 3-D data fusion between these snapshot models, in order to build incrementally a reliable 3-D model [6]. Ishiguro et al [7] also presented a strategy for establishing models of an unknown environment by a mobile robot. In their implementation, panoramic sensing was used to perceive the structure of the environment.…”

Section: B Survey Of Related Studiesmentioning

confidence: 99%

An incremental-learning-by-navigation approach to vision-based autonomous land vehicle guidance in indoor environments using vertical line information and multiweighted generalized Hough transform technique

Chen

Tsai

1998

IEEE Trans. Syst., Man, Cybern. B

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Abstract-An incremental-learning-by-navigation approach to visionbased autonomous land vehicle (ALV) guidance in indoor environments is proposed. The approach consists of three stages: initial learning, navigation, and model updating. In the initial learning stage, the ALV is driven manually, and environment images and other status data are recorded automatically. Then, an off-line procedure is performed to build an initial environment model. In the navigation stage, the ALV moves along the learned environment automatically, locates itself by model matching, and records necessary information for model updating. In the model updating stage, an off-line procedure is performed to refine the learned model. A more precise model is obtained after each navigationand-update iteration. Used environment features are vertical straight lines in camera views. A multiweighted generalized Hough transform is proposed for model matching. A real ALV was used as the testbed, and successful navigation experiments show the feasibility of the proposed approach.

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A strategy for acquiring an environmental model with panoramic sensing by a mobile robot

Cited by 18 publications

References 10 publications

Multispectral omnidirectional vision sensor: design, calibration, and utilization

Multispectral omnidirectional vision sensor: design, calibration, and utilization

VideoPlus: a method for capturing the structure and appearance of immersive environments

An incremental-learning-by-navigation approach to vision-based autonomous land vehicle guidance in indoor environments using vertical line information and multiweighted generalized Hough transform technique

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