An Evaluation of Image Feature Detectors and Descriptors for Robot Navigation

Schmidt, Adam; Kraft, Marek; Kasiński, Andrzej

doi:10.1007/978-3-642-15907-7_31

Cited by 56 publications

(38 citation statements)

References 11 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%

“…However, we think that the insights on illumination tolerance for different distance measures gained in this paper are an important step to make holistic methods more competitive, at least for the min-warping method investigated here. Moreover, for feature-based methods, a number of studies have been dedicated to the comparative evaluation of different feature detectors and descriptors for different applications [1,2,5,[17][18][19] indicating that there may be no single method suitable for all applications. A systematic study exploring the application of feature-based methods for local visual homing with panoramic images is not yet available, and would also be beyond the scope of this paper.…”

Section: Feature-based Vs Holistic Methodsmentioning

confidence: 99%

“…In these methods, an image patch is first transformed into an invariant feature descriptor which is then matched by some simpler distance measure (reviews: [1][2][3][4][5][17][18][19]); here the burden of invariance (not only against illumination changes, but also against occlusions, spatial distortions, or noise) is imposed on the transformation. SIFT descriptors [7] comprise summed gradient magnitudes for different orientations in a histogram-like representation.…”

Section: Illumination Invariance and Distance Measuresmentioning

confidence: 99%

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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%

Section: Feature-based Vs Holistic Methodsmentioning

confidence: 99%

Section: Illumination Invariance and Distance Measuresmentioning

confidence: 99%

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

2014

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“…In order to detect and extract landmark information from an image, we used Speed Up Robust Features (SURF) [33]. The work [34] recommended it after a comparison between several algorithms, showing that SURF provide results with a very low error rate. The projection of the ℎ landmark to the image frame can be formulated as follows:…”

Section: Observation Modelmentioning

confidence: 99%

A Low-Cost Consistent Vehicle Localization Based on Interval Constraint Propagation

Wang

Lambert

2018

Journal of Advanced Transportation

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Probabilistic techniques (such as Extended Kalman Filter and Particle Filter) have long been used to solve robotic localization and mapping problem. Despite their good performance in practical applications, they could suffer inconsistency problems. This paper proposes an interval analysis based method to estimate the vehicle pose (position and orientation) in a consistent way, by fusing low-cost sensors and map data. We cast the localization problem into an Interval Constraint Satisfaction Problem (ICSP), solved via Interval Constraint Propagation (ICP) techniques. An interval map is built when a vehicle embedding expensive sensors navigates around the environment. Then vehicles with low-cost sensors (dead reckoning and monocular camera) can use this map for ego-localization. Experimental results show the soundness of the proposed method in achieving consistent localization.

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“…So the criterion used for the evaluation of the detector or descriptor performance would be different. A number of interest point detectors and feature descriptors were evaluated for robot navigation separately in (Schmidt et al, 2010). The SURF was applied as one of the feature description method and outperformed others by comparing the ratio of inlier matches.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Sift and Surf for Vision Based Localization

Qu¹,

Soheilian²,

Habets³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Vision based localization is widely investigated for the autonomous navigation and robotics. One of the basic steps of vision based localization is the extraction of interest points in images that are captured by the embedded camera. In this paper, SIFT and SURF extractors were chosen to evaluate their performance in localization. Four street view image sequences captured by a mobile mapping system, were used for the evaluation and both SIFT and SURF were tested on different image scales. Besides, the impact of the interest point distribution was also studied. We evaluated the performances from for aspects: repeatability, precision, accuracy and runtime. The local bundle adjustment method was applied to refine the pose parameters and the 3D coordinates of tie points. According to the results of our experiments, SIFT was more reliable than SURF. Apart from this, both the accuracy and the efficiency of localization can be improved if the distribution of feature points are well constrained for SIFT.

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An Evaluation of Image Feature Detectors and Descriptors for Robot Navigation

Cited by 56 publications

References 11 publications

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

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

A Low-Cost Consistent Vehicle Localization Based on Interval Constraint Propagation

Evaluation of Sift and Surf for Vision Based Localization

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