5 research outputs found
Incrementally Learned Mixture Models for GNSS Localization
GNSS localization is an important part of today's autonomous systems,
although it suffers from non-Gaussian errors caused by non-line-of-sight
effects. Recent methods are able to mitigate these effects by including the
corresponding distributions in the sensor fusion algorithm. However, these
approaches require prior knowledge about the sensor's distribution, which is
often not available. We introduce a novel sensor fusion algorithm based on
variational Bayesian inference, that is able to approximate the true
distribution with a Gaussian mixture model and to learn its parametrization
online. The proposed Incremental Variational Mixture algorithm automatically
adapts the number of mixture components to the complexity of the measurement's
error distribution. We compare the proposed algorithm against current
state-of-the-art approaches using a collection of open access real world
datasets and demonstrate its superior localization accuracy.Comment: 8 pages, 5 figures, published in proceedings of IEEE Intelligent
Vehicles Symposium (IV) 201
CoBigICP: Robust and Precise Point Set Registration using Correntropy Metrics and Bidirectional Correspondence
In this paper, we propose a novel probabilistic variant of iterative closest
point (ICP) dubbed as CoBigICP. The method leverages both local geometrical
information and global noise characteristics. Locally, the 3D structure of both
target and source clouds are incorporated into the objective function through
bidirectional correspondence. Globally, error metric of correntropy is
introduced as noise model to resist outliers. Importantly, the close
resemblance between normal-distributions transform (NDT) and correntropy is
revealed. To ease the minimization step, an on-manifold parameterization of the
special Euclidean group is proposed. Extensive experiments validate that
CoBigICP outperforms several well-known and state-of-the-art methods.Comment: 6 pages, 4 figures. Accepted to IROS202