From grids to continuous occupancy maps through area kernels

Vido, Carlos E.O.; Ramos, Fábio

doi:10.1109/icra.2016.7487235

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…In our previous work [16], the inconsistency in discrete space was dealt with by using Markov random fields to regularise the grid. The static mapping methods [17][18][19][20][21][22][23], based on Gaussian Random Fields (GRFs), build smooth occupancy grid maps and predict the occupancy of unobserved space in continuous space. This paper is motivated by them and proposes a new dynamic mapping method based on GRF that is able to deal with continuous space instead of a discrete grid.…”

Section: Research Gap and Motivationmentioning

confidence: 99%

“…Gaussian processes and Bayesian Committee Machines are applied in [20] to recursively update occupancy maps and surface meshes. The multi-support kernel proposed in [21] enables traditional covariance functions to accept two-dimensional regions, reduces the size of covariance matrices, and accelerates Gaussian process inference and learning. A nested Bayesian committee machine is proposed to learn online 3D occupancy maps using Gaussian processes [22].…”

Section: Related Workmentioning

confidence: 99%

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HMM-Based Dynamic Mapping with Gaussian Random Fields

Barão

Rato

et al. 2022

Electronics

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This paper focuses on the mapping problem for mobile robots in dynamic environments where the state of every point in space may change, over time, between free or occupied. The dynamical behaviour of a single point is modelled by a Markov chain, which has to be learned from the data collected by the robot. Spatial correlation is based on Gaussian random fields (GRFs), which correlate the Markov chain parameters according to their physical distance. Using this strategy, one point can be learned from its surroundings, and unobserved space can also be learned from nearby observed space. The map is a field of Markov matrices that describe not only the occupancy probabilities (the stationary distribution) as well as the dynamics in every point. The estimation of transition probabilities of the whole space is factorised into two steps: The parameter estimation for training points and the parameter prediction for test points. The parameter estimation in the first step is solved by the expectation maximisation (EM) algorithm. Based on the estimated parameters of training points, the parameters of test points are obtained by the predictive equation in Gaussian processes with noise-free observations. Finally, this method is validated in experimental environments.

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Section: Research Gap and Motivationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

HMM-Based Dynamic Mapping with Gaussian Random Fields

Barão

Rato

et al. 2022

Electronics

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“…Reference [5] proposes a recursive method to update occupancy maps and surface meshes using Gaussian processes and Bayesian Committee Machines. A multi-support kernel, which enables traditional covariance functions to accept two-dimensional regions, is introduced to reduce the size of covariance matrices and accelerate Gaussian process inference and learning [6]. Reference [7] proposes a nested Bayesian committee machine to online learning 3D occupancy maps using Gaussian processes.…”

Section: Introductionmentioning

confidence: 99%

Gaussian random field-based log odds occupancy mapping

Barão

Rato

2018

2018 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR)

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This paper focuses on mapping problem with known robot pose in static environments and proposes a Gaussian random field-based log odds occupancy mapping (GRF-LOOM). In this method, occupancy probability is regarded as an unknown parameter and the dependence between parameters are considered. Given measurements and the dependence, the parameters of not only observed space but also unobserved space can be predicted. The occupancy probabilities in log odds form are regarded as a GRF. This mapping task can be solved by the wellknown prediction equation in Gaussian processes, which involves an inverse problem. Instead of the prediction equation, a new recursive algorithm is also proposed to avoid the inverse problem. Finally, the proposed method is evaluated in simulations. Index Terms-Binary Bayes filter, Gaussian random field, Log odds occupancy mapping.

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