Deriving and matching image fingerprint sequences for mobile robot localization

Lamon, Pierre; Nourbakhsh, Illah; Jensen, B.; Siegwart, Roland

doi:10.1109/robot.2001.932841

Cited by 59 publications

(53 citation statements)

References 19 publications

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“…The rest of the tests are replica of the tests in [6]. In this way, we could compare our results, in terms of success percentages, with their results.…”

Section: Tests and Resultsmentioning

confidence: 99%

“…In the same way, we can alter the value of d(i, j) in function of the similarity between characters p i and t j , that is to say, the more similar they are, the lower will be the penalty for mismatching. Making these simple modifications to the equations, we can achieve what Lamon et al [6] were looking for with their algorithm. In order to calculate the value D(i, j) we need the values for Fig.…”

Section: A Algorithm a (Global Alignment With K Differences Algorithm)mentioning

confidence: 99%

“…In a recent work [6], the authors take panoramic pictures from the environment by means of a mobile robot, and then they extract some features that are converted into strings. Their map is composed by a set of panoramic pictures taken at given space points, every certain distances, covering all the environment.…”

Section: Introductionmentioning

confidence: 99%

“…In [6] the authors suggested the use of string matching algorithms, but they discarded them and implemented their own minimum energy algorithm, based on dynamic programming. This decision was motivated by the fact that they wanted to consider the possibility of different types of mismatch errors, that is, they wanted to calibrate the rank of the discordances.…”

Section: Introductionmentioning

confidence: 99%

“…1. Panoramic image, its visual features and its string (this image, taken from [6], is used here with permission of the first author of that paper)…”

Section: Introductionmentioning

confidence: 99%

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Solving the mobile robot localization problem using string matching algorithms

Gonzalez-Buesa

Campos

2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566)

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Abstract-In this paper we address the mobile robot localization using some techniques borrowed from the Computational Biology community. The specific problem studied here is also known as the kidnapped robot problem. Our proposal is to solve this problem by string matching algorithms, which have experienced a large advance in the last years due to (for example) the Genoma Project. The paper uses three different algorithms to solve the mentioned problem and shows their advantages, such as the robustness of the results and the memory and time efficiency. These results are validated by real experimentation using panoramic images of indoor buildings, and compared and discussed with existing techniques that have been used over the same test-bed.

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“…The rest of the tests are replica of the tests in [6]. In this way, we could compare our results, in terms of success percentages, with their results.…”

Section: Tests and Resultsmentioning

confidence: 99%

Section: A Algorithm a (Global Alignment With K Differences Algorithm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…1. Panoramic image, its visual features and its string (this image, taken from [6], is used here with permission of the first author of that paper)…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Solving the mobile robot localization problem using string matching algorithms

Gonzalez-Buesa

Campos

2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566)

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Application Challenges from a Bird's‐Eye View

Scaramuzza

López²,

Imiya³

et al. 2017

Computer Vision in Vehicle Technology

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Computer Vision in Vehicle Technology focuses on computer vision as on-board technology, bringing together fields of research where computer vision is progressively penetrating: the automotive sector, unmanned aerial and underwater vehicles. It also serves as a reference for researchers of current developments and challenges in areas of the application of computer vision, involving vehicles such as advanced driver assistance (pedestrian detection, lane departure warning, traffic sign recognition), autonomous driving and robot navigation (with visual simultaneous localization and mapping) or unmanned aerial vehicles (obstacle avoidance, landscape classification and mapping, fire risk assessment).

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