2 research outputs found
A Rigorously Bayesian Beam Model and an Adaptive Full Scan Model for Range Finders in Dynamic Environments
This paper proposes and experimentally validates a Bayesian network model of
a range finder adapted to dynamic environments. All modeling assumptions are
rigorously explained, and all model parameters have a physical interpretation.
This approach results in a transparent and intuitive model. With respect to the
state of the art beam model this paper: (i) proposes a different functional
form for the probability of range measurements caused by unmodeled objects,
(ii) intuitively explains the discontinuity encountered in te state of the art
beam model, and (iii) reduces the number of model parameters, while maintaining
the same representational power for experimental data. The proposed beam model
is called RBBM, short for Rigorously Bayesian Beam Model. A maximum likelihood
and a variational Bayesian estimator (both based on expectation-maximization)
are proposed to learn the model parameters.
Furthermore, the RBBM is extended to a full scan model in two steps: first,
to a full scan model for static environments and next, to a full scan model for
general, dynamic environments. The full scan model accounts for the dependency
between beams and adapts to the local sample density when using a particle
filter. In contrast to Gaussian-based state of the art models, the proposed
full scan model uses a sample-based approximation. This sample-based
approximation enables handling dynamic environments and capturing
multi-modality, which occurs even in simple static environments
Improved Likelihood Models for Probabilistic Localization based on Range Scans
Abstract β Range sensors are popular for localization since they directly measure the geometry of the local environment. Another distinct benefit is their typically high accuracy and spatial resolution. It is a well-known problem, however, that the high precision of these sensors leads to practical problems in probabilistic localization approaches such as Monte Carlo localization (MCL), because the likelihood function becomes extremely peaked if no means of regularization are applied. In practice, one therefore artificially smoothes the likelihood function or only integrates a small fraction of the measurements. In this paper we present a more fundamental and robust approach, that provides a smooth likelihood model for entire range scans. Additionally, it is location-dependent. In practical experiments we compare our approach to previous methods and demonstrate that it leads to a more robust localization. I