Image-based homing

Hong, Jin‐Woo; Tan, X.; Pinette, Brian; Weiss, Richard; Riseman, Edward M.

doi:10.1109/37.120451

Cited by 75 publications

(1 citation statement)

References 17 publications

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“…Special cases of these solutions give the shapes of mirrors and the conditions to be met. The mirror used can be plane (Nalwa, 1996), hyperboloid (Rees, 1970;Yamazawa et al, 1993), spherical (Hong et al, 1992), paraboloid (Nayar, 1997) or conical (Yagi and Kawato, 1990;Lin and Bajcsy, 2001). Sturm and Ramalingam (2004), Ramalingam et al (2005) proposed a method of generic calibration applicable to both types of cameras: with or without a single view point.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Image Matching by using the Proximity Criterion: Application to Omnidirectional and Perspective Images

Guelzim¹,

Hammouch²,

Aboutajdine³

2011

Journal of Computer Science

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Problem statement: In computer vision, matching is an important phase for several applications (object reconstruction, robot navigation ...). The similarity measures used provided results which could be improved. Approach: This research proposed to improve image matching by using the proximity criterion. The similarity measures used mutual information and correlation coefficient. The matching was done between neighborhoods of points of interest extracted from the images. The second chance algorithm was also applied. We have worked in case which the sensor had a slight displacement between two images. The tests were performed on omnidirectional and perspective grayscale images. Results: The improvement by introducing the proximity criterion reached 15.9% for non-noised perspective images, 32.1% for noised perspective images, 47.69% for non-noised omnidirectional images and 58.5% for noised omnidirectional images. Conclusion/Recommendations: The introduction of the proximity criterion has significantly improved the performance of the matching. The method is recommended in mobile robotics, knowing that a good matching leads to a better location and better movement of the robot

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Section: Introductionmentioning

confidence: 99%

Improvement of Image Matching by using the Proximity Criterion: Application to Omnidirectional and Perspective Images

Guelzim¹,

Hammouch²,

Aboutajdine³

2011

Journal of Computer Science

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Cataglyphis: An autonomous sample return rover

Ohi

Lassak

et al. 2017

Journal of Field Robotics

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This paper presents the design of Cataglyphis, a research rover that won the NASA Sample Return Robot Centennial Challenge in 2015. During the challenge, Cataglyphis was the only robot that was able to autonomously find, retrieve, and return multiple types of samples in a large natural environment without using Earth‐specific sensors such as GPS and magnetic compasses. It navigates through a fusion of measurements collected from inertial sensors, wheel encoders, a nodding Lidar, a set of ranging radios, a camera on a panning platform, and a sun sensor. In addition to visual detection of a homing beacon, computer vision algorithms provide the sample detection, identification, and localization capabilities, with low false positive and false negative rates demonstrated during the competition. The mission planning and control software enables robot behaviors, determines sequences of actions, and helps the robot to recover from various failure conditions. A compliant, under‐actuated manipulator conforms to the natural terrain before picking up samples of various size, weight, and shape.

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Agnostic Learning of Geometric Patterns

Goldman¹,

Kwek²,

Scott³

2001

Journal of Computer and System Sciences

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) discussed how the problem of recognizing a landmark from a one-dimensional visual image might be mapped to that of learning a onedimensional geometric pattern and gave a PAC algorithm to learn that class. In this paper, we present an efficient online agnostic learning algorithm for learning the class of constant-dimensional geometric patterns. Our algorithm can tolerate both classification and attribute noise. By working in higher dimensional spaces we can represent more features from the visual image in the geometric pattern. Our mapping of the data to a geometric pattern and, hence, our learning algorithm are applicable to any data representable as a constant-dimensional array of values, e.g., sonar data, temporal difference information, amplitudes of a waveform, or other pattern recognition data. To our knowledge, these classes of patterns are more complex than any class of geometric patterns previously studied. Also, our results are easily adapted to learn the union of fixed-dimensional boxes from multiple-instance examples. Finally, our algorithms are tolerant of concept shift, where the target concept that labels the examples can change over time. Academic Press

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Image-based homing

Cited by 75 publications

References 17 publications

Improvement of Image Matching by using the Proximity Criterion: Application to Omnidirectional and Perspective Images

Improvement of Image Matching by using the Proximity Criterion: Application to Omnidirectional and Perspective Images

Cataglyphis: An autonomous sample return rover

Agnostic Learning of Geometric Patterns

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