Environment identification by comparing maps of landmarks

Guttman, Jerry; Fukuchi, Masaki; Sabe, Kohtaro

doi:10.1109/robot.2003.1241606

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Several scan-match algorithms can provide this, for example, Gutmann et al [11] use point matches to lines extracted in the reference scan, and return a Gaussian estimate of error. More recently, the correlation method of Konolige and Chou [17], extended by Olson [22], provides an efficient method for finding the globally best match within a given range, while returning …”

Section: Scan Matchingmentioning

confidence: 99%

Efficient Sparse Pose Adjustment for 2D mapping

Konolige

Grisetti

Kümmerle

et al. 2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

333

176

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Abstract-Pose graphs have become a popular representation for solving the simultaneous localization and mapping (SLAM) problem. A pose graph is a set of robot poses connected by nonlinear constraints obtained from observations of features common to nearby poses. Optimizing large pose graphs has been a bottleneck for mobile robots, since the computation time of direct nonlinear optimization can grow cubically with the size of the graph. In this paper, we propose an efficient method for constructing and solving the linear subproblem, which is the bottleneck of these direct methods. We compare our method, called Sparse Pose Adjustment (SPA), with competing indirect methods, and show that it outperforms them in terms of convergence speed and accuracy. We demonstrate its effectiveness on a large set of indoor real-world maps, and a very large simulated dataset. Open-source implementations in C++, and the datasets, are publicly available.

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Section: Scan Matchingmentioning

confidence: 99%

Efficient Sparse Pose Adjustment for 2D mapping

Konolige

Grisetti

Kümmerle

et al. 2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

333

176

View full text Add to dashboard Cite

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“…See the overview of earlier solutions by Borenstein et al [6]. For example, markers can be placed on walls and on the floor [7], installed in the ceiling [8], or require built-in intelligence to make them easier to sense [9].…”

Section: Introductionmentioning

confidence: 99%

Challenges of designing a low-cost indoor localization system using active beacons

Gutmann

Fong

Chiu

et al. 2013

2013 IEEE Conference on Technologies for Practical Robot Applications (TePRA)

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Localization is very helpful for goal-oriented navi gation of a mobile robot. In this article, we describe the challenges we faced when designing a low-cost indoor localization system that can be employed on consumer and domestic robots for the systematic navigation in household environments. Our system uses active beacons that project a pair of infrared (IR) spots onto the ceiling and a sensor on the robot which observes them.In order to reduce cost, we designed a 3-diode sensor, i.e. a three pixel camera, that measures directions to the spots. In contrast to e.g. a typical camera with VGA or higher resolution that would allow for a precise tracking of the beacon spots, our low-cost sensor suffers from multi-path, i.e. light not only reaches the sensor directly but also through reflections from walls and other furniture. We tackle this problem by a localization method that learns the light distribution in the room through a simultaneous localization and mapping (SLAM) approach. Our experiments provide numbers on the accuracy and consistency of this method. The presented system is implemented on our Mint robot equipped with an ARM 7 processor and 64 kByte of RAM for the autonomous cleaning of floors.

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