Global localization with non-quantized local image features

Campos, Francisco M.; Correia, Luís; Calado, João Manuel Ferreira

doi:10.1016/j.robot.2012.05.015

Cited by 8 publications

(3 citation statements)

References 48 publications

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“…In [31], researchers investigate the potential to improve the nonquantized (NQ) method, by exploiting the entropy-discriminativity relation. In this work they investigate the nonquantized representation as a solution to the global localization problem.…”

Section: Other Methodsmentioning

confidence: 99%

Mobile Robot Localization: A Review of Probabilistic Map-Based Techniques

Malagon-Soldara

Toledano‐Ayala

Soto-Zarazúa

et al. 2015

IJRA

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This work presents a comprehensive review of current probabilistic developments used to calculate position by mobile robots in indoor environments. In this calculation, best known as localization, it is necessary to develop most of the tasks delegated to the mobile robot. It is then crucial that the methods used for position calculations be as precise as possible, and accurately represent the location of the robot within a given environment. The research community has devoted a considerable amount of time to provide solutions for the localization problem. Several methodologies have been proposed the most common of which are based in the Bayes rule. Other methodologies include the Kalman filter and the Monte Carlo localization filter wich will be addressed in next sections. The major contribution of this review rests in offering a wide array of techniques that researchers can choose. Therefore, method-sensor combinations and their main advantages are displayed.

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Section: Other Methodsmentioning

confidence: 99%

Mobile Robot Localization: A Review of Probabilistic Map-Based Techniques

Malagon-Soldara

Toledano‐Ayala

Soto-Zarazúa

et al. 2015

IJRA

View full text Add to dashboard Cite

show abstract

“…The Visual data driven approach (VIDAR) is proposed as an appearance-based SLAM approach, while estimating the accurate location of the robot without accumulated error. Unlike non-quantized global localization [15] , the approximation of the kernel density estimator (finding the nearest neighbour among SIFT descriptors) is usually failed in our scenario. Additionally, their method cannot satisfy the requirements of metric localization via place classification.…”

Section: Motivations and Backgroundmentioning

confidence: 99%

Visual Data Driven Approach for Metric Localization in Substation

Liu

et al. 2015

Chin. j. electron.

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“…The distribution quality, discussed in Section 2, is significant for visual homing and has been taken into account during feature selection process, thus every selected feature is surely relevant to the homing task. The mutual information has been applied for navigation tasks [ 6 , 20 , 25 , 26 ], however, it is used in a different way in this paper. The mutual information can be used to measure the quantity of information shared by two random variables.…”

Section: Proposed Approaches To Feature Selection and Updatingmentioning

confidence: 99%

Feature Optimization for Long-Range Visual Homing in Changing Environments

Zhu

Xue-yi

Cai

2014

Sensors

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This paper introduces a feature optimization method for robot long-range feature-based visual homing in changing environments. To cope with the changing environmental appearance, the optimization procedure is introduced to distinguish the most relevant features for feature-based visual homing, including the spatial distribution, selection and updating. In the previous research on feature-based visual homing, less effort has been spent on the way to improve the feature distribution to get uniformly distributed features, which are closely related to homing performance. This paper presents a modified feature extraction algorithm to decrease the influence of anisotropic feature distribution. In addition, the feature selection and updating mechanisms, which have hardly drawn any attention in the domain of feature-based visual homing, are crucial in improving homing accuracy and in maintaining the representation of changing environments. To verify the feasibility of the proposal, several comprehensive evaluations are conducted. The results indicate that the feature optimization method can find optimal feature sets for feature-based visual homing, and adapt the appearance representation to the changing environments as well.

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Global localization with non-quantized local image features

Cited by 8 publications

References 48 publications

Mobile Robot Localization: A Review of Probabilistic Map-Based Techniques

Mobile Robot Localization: A Review of Probabilistic Map-Based Techniques

Visual Data Driven Approach for Metric Localization in Substation

Feature Optimization for Long-Range Visual Homing in Changing Environments

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