Robust localization using panoramic view-based recognition

Jogan, Matjaž; Leonardis, Aleš

doi:10.1109/icpr.2000.902882

Cited by 47 publications

(36 citation statements)

References 7 publications

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“…Most of the approaches have used Principle Component Analysis (PCA) for building efficient representations and for subsequent recognition. The approach has led to a variety of successful applications, e.g., human face recognition [22,2], visual inspection [25], visual positioning and tracking of robot manipulators [15], illumination planning [14], mobile robot localization [11], and background modeling [17]. However, the standard way to perform recognition, based on projections, is prone to errors in the case of non-Gaussian noise, e.g., occlusions, varying illumination conditions, and cluttered background in the input images.…”

Section: Introductionmentioning

confidence: 99%

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A Robust PCA Algorithm for Building Representations from Panoramic Images

Skočaj

Bischof

Leonardis

2002

Computer Vision — ECCV 2002

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Abstract. Appearance-based modeling of objects and scenes using PCA has been successfully applied in many recognition tasks. Robust methods which have made the recognition stage less susceptible to outliers, occlusions, and varying illumination have further enlarged the domain of applicability. However, much less research has been done in achieving robustness in the learning stage. In this paper, we propose a novel robust PCA method for obtaining a consistent subspace representation in the presence of outlying pixels in the training images. The method is based on the EM algorithm for estimation of principal subspaces in the presence of missing data. By treating the outlying points as missing pixels, we arrive at a robust PCA representation. We demonstrate experimentally that the proposed method is efficient. In addition, we apply the method to a set of panoramic images to build a representation that enables surveillance and view-based mobile robot localization.

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Section: Introductionmentioning

confidence: 99%

“…Let us consider, for example, a case of a view-based localization of a mobile platform using a set of panoramic images to represent the environment [8,9,1,11]. Due to the wide field-of-view, it is almost impossible to obtain training images without outliers (e.g., people moving around).…”

Section: Introductionmentioning

confidence: 99%

A Robust PCA Algorithm for Building Representations from Panoramic Images

Skočaj

Bischof

Leonardis

2002

Computer Vision — ECCV 2002

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show abstract

“…Other interesting approaches can be found in the Mid-Size league 6 : as the hardware of a Mid-Size robot is only limited to what it can carry, both different types of sensors as well as different classes of algorithms become feasible. Most Midsize robots use omni-directional high-quality cameras whereof each camera image carries enough information to localize the robot almost perfectly [5].…”

Section: Related Workmentioning

confidence: 99%

Panoramic Localization in the 4-Legged League

Sturm

Rossum

Visser

2007

RoboCup 2006: Robot Soccer World Cup X

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Abstract. The abilities of mobile robots depend greatly on the performance of basic skills such as vision and localization. Although great progress has been made in the 4-Legged league in the past years, the performance of many of those approaches completely depends on the artificial environment conditions established on a 4-Legged soccer field. In this article, an algorithm is introduced that can provide localization information based on the natural appearance of the surroundings of the field. The algorithm starts making a scan of the surroundings by turning head and body of the robot on a certain spot. The robot learns the appearance of the surroundings at that spot by storing color transitions at different angles in a panoramic index. The stored panoramic appearance can be used to determine the rotation (including a confidence value) relative to the learned spot for other points on the field. The applicability of this kind of localization for more natural environments is demonstrated in two environments other than the official 4-Legged league field.

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“…Some memory-based approaches employ panoramic imaging systems as we do here (e.g. [14,15]). If the agent never has to leave the vicinity of the goal then the feature tracking approach of [16] can be employed for homing (this approach also uses corners as we do here).…”

Section: Visual Homingmentioning

confidence: 99%

“…al employ formal computer vision techniques to determine the epipolar geometry relating the snapshot and current locations and move directly home [12]. Memory-based approaches represent the environment by a set of images and associated home vectors [13,14,15]. Some memory-based approaches employ panoramic imaging systems as we do here (e.g.…”

Section: Visual Homingmentioning

confidence: 99%

Anatomy and Physiology of an Artificial Vision Matrix

Vardy

Oppacher

2004

Biologically Inspired Approaches to Advanced Information Technology

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Abstract. We present a detailed account of the processing that occurs within a biologically-inspired model for visual homing. The Corner Gradient Snapshot Model (CGSM) initially presented in [1] was inspired by the snapshot model [2] which provided an algorithmic explanation for the ability of honeybees to return to a place of interest after being displaced. The concept of cellular vision is introduced as a constraint on processing. A cellular vision matrix processes visual information using retinotopically arranged layers of low-level processing elements interacting locally. This style of processing reflects general principles known of visual processing throughout the animal kingdom. From a technical standpoint, this style of processing is inherently parallel. Here we describe a cellular vision matrix which implements CGSM and illustrate how this matrix obeys cellular vision. Some new comparative results are presented and it is found that CGSM's performance degrades gracefully with environmental modification and occlusion.

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Robust localization using panoramic view-based recognition

Abstract: Abstract

Cited by 47 publications

References 7 publications

A Robust PCA Algorithm for Building Representations from Panoramic Images

A Robust PCA Algorithm for Building Representations from Panoramic Images

Panoramic Localization in the 4-Legged League

Anatomy and Physiology of an Artificial Vision Matrix

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