2012 IEEE/RSJ International Conference on Intelligent Robots and Systems 2012
DOI: 10.1109/iros.2012.6385629
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Independent Markov chain occupancy grid maps for representation of dynamic environment

Abstract: In this paper we propose a new grid based approach to model a dynamic environment. Each grid cell is assumed to be an independent Markov chain (iMac) with two states. The state transition parameters are learned online and modeled as two Poisson processes. As a result, our representation not only encodes the expected occupancy of the cell, but also models the expected dynamics within the cell. The paper also presents a strategy based on recency weighting to learn the model parameters from observations that is a… Show more

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Cited by 76 publications
(59 citation statements)
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“…Saarinen at al. [5] model a dynamic environment as an occupancy grid with associated independent Markov chains for every cell on the grid. Patch Maps [6] represents the different observed states of parts of the map and selects the one most similar to the robot's observation for localization.…”
Section: Related Workmentioning
confidence: 99%
“…Saarinen at al. [5] model a dynamic environment as an occupancy grid with associated independent Markov chains for every cell on the grid. Patch Maps [6] represents the different observed states of parts of the map and selects the one most similar to the robot's observation for localization.…”
Section: Related Workmentioning
confidence: 99%
“…We first explain about the IMAC model [5] . The proposed algorithm is given in the next sub-sections: the grid update algorithm and the propose method for anomalous stop detection.…”
Section: Approach For Map Generation and Anomaly Detectionmentioning
confidence: 99%
“…The IMAC [5] is an occupancy grid map based on the proposed model of Luber [4] . Each grid has two states, which being in occupied or free.…”
Section: Independent Markov Chain Occupancy Grid Map (Imac)mentioning
confidence: 99%
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“…In this paper we present a method that allows an autonomous robot to iteratively learn to distinguish between 1 The authors are all with the Centre for Autonomous Systems at KTH Royal Institute of Technology, Stockholm, SE-100 44, Sweden.…”
Section: Introductionmentioning
confidence: 99%