Navigation from Uncalibrated Monocular Vision

Guerrero, J. J.; Sagüés, Carlos

doi:10.1016/s1474-6670(17)44110-3

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Various visual features (corners, edges, color, texture) and visual cues (e.g., feature motion, parallax) have been employed to facilitate navigational functions with uncalibrated cameras. These include navigation down corridors both by using the focus of expansion of nonvertical scene lines [15] and wide field peripheral flow [3]. Obstacle detection using the projective invariants associated with three horizontal tracked lines and the vanishing lines of planes (ground plane and obstacle planes) has been applied to road scenes successfully [5].…”

Section: Robust Multifeature Multicue Vision Systemsmentioning

confidence: 99%

Mobile Robot Visual Navigation Using Multiple Features

Pears

Liang

Chen

2005

EURASIP J. Adv. Signal Process.

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We propose a method to segment the ground plane from a mobile robot's visual field of view and then measure the height of nonground plane features above the mobile robot's ground plane. Thus a mobile robot can determine what it can drive over, what it can drive under, and what it needs to manoeuvre around. In addition to obstacle avoidance, this data could also be used for localisation and map building. All of this is possible from an uncalibrated camera (raw pixel coordinates only), but is restricted to (near) pure translation motion of the camera. The main contributions are (i) a novel reciprocal-polar (RP) image rectification, (ii) ground plane segmentation by sinusoidal model fitting in RP-space, (iii) a novel projective construction for measuring affine height, and (iv) an algorithm that can make use of a variety of visual features and therefore operate in a wide variety of visual environments.

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Section: Robust Multifeature Multicue Vision Systemsmentioning

confidence: 99%

Mobile Robot Visual Navigation Using Multiple Features

Pears

Liang

Chen

2005

EURASIP J. Adv. Signal Process.

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“…However, we have noted that there are several disadvantages with using approaches which directly use four or more image correspondences and, as an alternative, we propose a horizon line -vanishing point (2-point) method, which exhibits greater robustness when the robot undergoes pure translation. Consider two camera centered coordinate systems, frame i and frame 2, so that we can write X2=RX1+T, (2) where X1 and X2 are the coordinates of the same 3D point, expressed in frames i and 2 respectively and where R and T are the rotation and the translation matrices encoding the relative position of the two coordinate systems. Now assume that X1 is a point on the plane defined by: AX1+BY1+CZ1+i=O.…”

Section: Grouping Coplanar Features Using Homographies 41 Computatimentioning

confidence: 99%

Ground plane segmentation from multiple visual cues

Liang

Pears

2002

SPIE Proceedings

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An approach which uses multiple sources of visual information (or visual cues) to identify and segment the ground plane in indoor mobile robot visual navigation applications is presented. Information from color, contours and corners and their motion are applied, in conjunction with planar homography relations, to identify the navigable area of the ground, which may be textured or non-textured. We have developed new algorithms for both the computation of the homography, in which a highly stable two point method for pure translation is proposed, and the region growing. Also, a new method for applying the homography to measure the height of a visual feature to the ground using an uncalibrated camera is also developed. Regions are segmented by color and also by their sizes and geometric relation and these region boundarys are extracted as contours. By controlled manoeuvres of a mobile robot, the methods of coplanar feature grouping developed in this paper are not only applicable to corner correspondences but also to contours. This leads to robust, accurate segmentation of the ground plane from the other image regions. Results are presented which show the validity of the approach.

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“…In fact, there have been some attempts on using this type of information. However, in prior work such as [9,10], the vertical lines were simply used as an ordinary feature, and usually the image plane is assumed to be vertical.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Vertical Lines in Vision-Based Navigation for Mobile Robot Platforms

Zhou

2007

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07

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Vertical line-like structures are abundant in man-made environments, such as the vertical edges of buildings in an outdoor environment and door frames and vertical edges of furniture in an indoors environment. These ubiquitous line or line-like structures may provide useful information for mobile robot navigation. In this paper, we propose an approach to estimating the camera orientation based on detected vertical lines. Further we use the estimated camera orientation in one key problem of mobile robot navigation: ground/obstacle detection. Our method is theoretically wellgrounded and yet simple and practical in implementation.

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Navigation from Uncalibrated Monocular Vision

Cited by 5 publications

References 9 publications

Mobile Robot Visual Navigation Using Multiple Features

Mobile Robot Visual Navigation Using Multiple Features

Ground plane segmentation from multiple visual cues

Exploiting Vertical Lines in Vision-Based Navigation for Mobile Robot Platforms

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