Paper Info
Title
Accurate Mix-Norm-Based Scan Matching
Abstract
Highly accurate mapping and localization is of prime importance for mobile robotics, and its core lies in efficient scan matching. Previous research are focusing on designing a robust objective function and the residual error distribution is often ignored or simply assumed as unitary or mixture of simple distributions. In this paper, a mixture of exponential power (MoEP) distributions is proposed to approximate the residual error distribution. The objective function induced by MoEP-based residual error modelling ensembles a mix-norm-based scan matching (MiNoM), which enhances the matching accuracy and convergence characteristic. Both the parameters of transformation (rotation and translation) and residual error distribution are estimated efficiently via an EM-like algorithm. The optimization of MiNoM is iteratively achieved via two phases: An on-line parameter learning (OPL) phase to learn residual error distribution for better representation according to the likelihood field model (LFM), and an iteratively reweighted least squares (IRLS) phase to attain transformation for accuracy and efficiency. Extensive experimental results validate that the proposed MiNoM outperforms several state-of-the-art scan matching algorithms in both convergence characteristic and matching accuracy.
Year
DOI
Venue
2018
10.1109/IROS.2018.8594278
2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
Field
DocType
ISSN
Convergence (routing),Prime (order theory),Residual,Computer vision,Exponential function,Computer science,Algorithm,Iteratively reweighted least squares,Parameter learning,Artificial intelligence,Linear programming,Robotics
Conference
2153-0858
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Di Wang1204.74
J. Xue254257.57
Zhongxing Tao300.68
Yang Zhong4594.17
Dixiao Cui5182.41
Shaoyi Du635740.68
Nanning Zheng76521.36