TY - JOUR

T1 - Odometry under Interval Uncertainty: Towards Optimal Algorithms, with Potential Application to Self-Driving Cars and Mobile Robots

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

Y1 - 2020/6

N2 - 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.

AB - 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.

M3 - Article

VL - 27

SP - 12

EP - 20

JO - Reliable Computing

JF - Reliable Computing

SN - 1385-3139

ER -