Mapping dynamic environments using Markov random field models

Li, Hongjun; Barão, Miguel; Rato, Luís

doi:10.23919/iconac.2018.8749092

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…Another approach to model the changes in the occupancy between adjacent cells with the use of HMM was presented by Li et al (2018). However, in contrast to previously presented methods, the work of Li et al (2018) does not treat cells in separation from their neighbours but utilizes Markov Random Field (MRF). The MRF models the interaction between the adjacent cells, in a similar way as IOHMM (Wang et al, 2015).…”

Section: Generalizingmentioning

confidence: 99%

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Survey of maps of dynamics for mobile robots

Kucner

Magnusson

Mghames

et al. 2023

The International Journal of Robotics Research

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Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.

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Section: Generalizingmentioning

confidence: 99%

“…The key difference is that Li et al (2018) do not model the direction of the motion explicitly. The works presented so far in this section focused on modelling the dynamics in the metric space.…”

Section: Surveymentioning

confidence: 99%

Survey of maps of dynamics for mobile robots

Kucner

Magnusson

Mghames

et al. 2023

The International Journal of Robotics Research

View full text Add to dashboard Cite

show abstract

“…Normally, the correlation between parameters in space is not considered in HMM-based methods and inconsistent maps will be produced. 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.…”

Section: Research Gap and Motivationmentioning

confidence: 99%

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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Mapping dynamic environments using Markov random field models

Cited by 2 publications

References 9 publications

Survey of maps of dynamics for mobile robots

Survey of maps of dynamics for mobile robots

HMM-Based Dynamic Mapping with Gaussian Random Fields

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