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

authored by
R. Voges, B. Wagner, V. Kreinovich
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.

Organisation(s)
Real Time Systems Section
External Organisation(s)
University of Texas at El Paso
Type
Article
Journal
Reliable Computing
Volume
27
Pages
12-20
No. of pages
9
ISSN
1385-3139
Publication date
06.2020
Publication status
Published
Electronic version(s)
https://reliable-computing.org/reliable-computing-27-pp-012-020.pdf (Access: Open)
 

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