SIFT, SURF &amp; seasons: Appearance-based long-term localization in outdoor environments

Valgren, Christoffer; Lilienthal, Achim J.

doi:10.1016/j.robot.2009.09.010

Cited by 193 publications

(118 citation statements)

References 17 publications

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“…A number of feature detectors and descriptors have been developed, examples being the wide-spread FAST detector [6] and the SIFT [7] and SURF detectors and descriptors [8], and the modern binary descriptors such as BRIEF [9,10], ORB [11], or FREAK [12]. In robot navigation, matches between features are either used for estimating ego-motion between camera postures from two images (such as in visual odometry, see [13,14]), for estimating the metrical position of the corresponding landmark in a geometrical map [15][16][17][18], or for place recognition [19]. Together with subsequent outlier processing (like RANSAC) and n-point methods [14], feature-based methods can reliably estimate the relative posture between two images in 5 dimensions (up to scale).…”

Section: Feature-based Vs Holistic Methodsmentioning

confidence: 99%

Illumination Tolerance for Visual Navigation with the Holistic Min-Warping Method

2014

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Holistic visual navigation methods are an emerging alternative to the ubiquitous feature-based methods. Holistic methods match entire images pixel-wise instead of extracting and comparing local feature descriptors. In this paper we investigate which pixel-wise distance measures are most suitable for the holistic min-warping method with respect to illumination invariance. Two novel approaches are presented: tunable distance measures-weighted combinations of illumination-invariant and illumination-sensitive terms-and two novel forms of "sequential" correlation which are only invariant against intensity shifts but not against multiplicative changes. Navigation experiments on indoor image databases collected at the same locations but under different conditions of illumination demonstrate that tunable distance measures perform optimally by mixing their two portions instead of using the illumination-invariant term alone. Sequential correlation performs best among all tested methods, and as well but much faster in an approximated form. Mixing with an additional illumination-sensitive term is not necessary for sequential correlation. We show that min-warping with approximated sequential correlation can successfully be applied to visual navigation of cleaning robots.

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Section: Feature-based Vs Holistic Methodsmentioning

confidence: 99%

Illumination Tolerance for Visual Navigation with the Holistic Min-Warping Method

2014

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“…Glover et al [11] measured the performance of various SLAM algorithms using data sets from different times of the day. Valgren and Lilienthal [24] topological localization. Data sets were acquired over long periods of time to capture the natural seasonal changes in an outdoor environment.…”

Section: Related Workmentioning

confidence: 99%

“…We also analyze three environmental parameters: time of day (morning, midday, afternoon, dusk), sunlight (sunny, cloudy), and environment (commercial, residential, rural). We do not consider seasonal variations and adverse weather conditions such as fog, rain, and snow, or nighttime driving, which have been studied previously [6], [17], [24].…”

Section: B Data Setsmentioning

confidence: 99%

“…Specifically, we consider three environmental factors: environment type, time of day, and sunlight conditions. The effect of seasonal variation has been studied previously [6], [24]. To analyze the effect of environment type on localization accuracy, we separated the data from test route 1 into three basic environmental types: rural/parklike, commercial/downtown, and residential/suburban.…”

Section: Environmental Factorsmentioning

confidence: 99%

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Understanding how camera configuration and environmental conditions affect appearance-based localization

Bansal

Badino

Huber

2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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Abstract-Localization is a central problem for intelligent vehicles. Visual localization can supplement or replace GPSbased localization approaches in situations where GPS is unavailable or inaccurate. Although visual localization has been demonstrated in a variety of algorithms and systems, the problem of how to best configure such a system remains largely an open question. Design choices, such as "where should the camera be placed?" and "how should it be oriented?" can have substantial effect on the cost and robustness of a fielded intelligent vehicle. This paper analyzes how different sensor configuration parameters and environmental conditions affect visual localization performance with the goal of understanding what causes certain configurations to perform better than others and providing general principles for configuring systems for visual localization. We ground the investigation using extensive field testing of a visual localization algorithm, and the data sets used for the analysis are made available for comparative evaluation.

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“…The work presented in [38] compares SIFT and SURF features, proposes the use of higher resolution images, and suggests introducing additional geometric constraints for localization. Rosen et al [33] derive a stochastic filter that uses Survival Analysis [10] for modeling the lifetime of a feature.…”

Section: Related Workmentioning

confidence: 99%

Will It Last? Learning Stable Features for Long-Term Visual Localization

Dymczyk

Stumm

Nieto

et al. 2016

2016 Fourth International Conference on 3D Vision (3DV)

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Figure 1: An overview of the proposed algorithm: A classifier is used to decide whether a particular visual feature is expected to be persistent or not. Our method uses full image information as input and helps to maintain compact stable-over-time maps that can be used for life-long localization. AbstractAn increasing number of simultaneous localization and mapping (SLAM) systems are using appearance-based localization to improve the quality of pose estimates. However, with the growing time-spans and size of the areas we want to cover, appearance-based maps are often becoming too large to handle and are consisting of features that are not always reliable for localization purposes. This paper presents a method for selecting map features that are persistent over time and thus suited for long-term localization. Our methodology relies on a CNN classifier based on image patches and depth maps for recognizing which features are suitable for life-long matchability. Thus, the classifier not only considers the appearance of a feature but also takes into account its expected lifetime. As a result, our feature selection approach produces more compact maps with a high fraction of temporally-stable features compared to the current state-of-the-art, while rejecting unstable features that typically harm localization. Our approach is validated on indoor and outdoor datasets, that span over a period of several months.

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SIFT, SURF & seasons: Appearance-based long-term localization in outdoor environments

Cited by 193 publications

References 17 publications

Illumination Tolerance for Visual Navigation with the Holistic Min-Warping Method

Illumination Tolerance for Visual Navigation with the Holistic Min-Warping Method

Understanding how camera configuration and environmental conditions affect appearance-based localization

Will It Last? Learning Stable Features for Long-Term Visual Localization

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