Odometry under Interval Uncertainty: Towards Optimal Algorithms, with Potential Application to Self-Driving Cars and Mobile Robots

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Original languageEnglish
Pages (from-to)12-20
Number of pages9
JournalReliable Computing
Volume27
Publication statusPublished - Jun 2020

Abstract

In many practical applications ranging from self-driving cars to industrial application of mobile robots, it is important to take interval uncertainty into account when performing odometry, i.e., when estimating how our position and orientation (“pose”) changes over time. In particular, one of the important aspects of this problem is detecting mismatches (outliers). In this paper, we describe an algorithm to compute the rigid body transformation, including a provably optimal sub-algorithm for detecting mismatches.

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Odometry under Interval Uncertainty: Towards Optimal Algorithms, with Potential Application to Self-Driving Cars and Mobile Robots. / Voges, R.; Wagner, B.; Kreinovich, V.
In: Reliable Computing, Vol. 27, 06.2020, p. 12-20.

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abstract = "In many practical applications ranging from self-driving cars to industrial application of mobile robots, it is important to take interval uncertainty into account when performing odometry, i.e., when estimating how our position and orientation (“pose”) changes over time. In particular, one of the important aspects of this problem is detecting mismatches (outliers). In this paper, we describe an algorithm to compute the rigid body transformation, including a provably optimal sub-algorithm for detecting mismatches.",
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note = "This work was supported by the German Research Foundation (DFG) as part of the Research Training Group i.c.sens (grant RTG 2159), and by the Leibniz University of Hannover. It was also supported in part by the US National Science Foundation grants 1623190 (A Model of Change for Preparing a New Generation for Professional Practice in Computer Science) and HRD-1242122 (Cyber-ShARE Center of Excellence).",
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AU - Voges, R.

AU - Wagner, B.

AU - Kreinovich, V.

N1 - This work was supported by the German Research Foundation (DFG) as part of the Research Training Group i.c.sens (grant RTG 2159), and by the Leibniz University of Hannover. It was also supported in part by the US National Science Foundation grants 1623190 (A Model of Change for Preparing a New Generation for Professional Practice in Computer Science) and HRD-1242122 (Cyber-ShARE Center of Excellence).

PY - 2020/6

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M3 - Article

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JO - Reliable Computing

JF - Reliable Computing

SN - 1385-3139

ER -

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