Bayesian Learning of Occupancy Grids Article Swipe
Christopher Robbiano
,
Edwin K. P. Chong
,
M.R. Azimi-Sadjadi
,
Louis L. Scharf
,
Ali Pezeshki
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/tits.2020.3019813
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/tits.2020.3019813
Occupancy grids encode for hot spots on a map that is represented by a two dimensional grid of disjoint cells. The problem is to recursively update the probability that each cell in the grid is occupied, based on a sequence of sensor measurements from a moving platform. In this paper, we provide a new Bayesian framework for generating these probabilities that does not assume statistical independence between the occupancy state of grid cells. This approach is made analytically tractable through the use of binary asymmetric channel models that capture the errors associated with observing the occupancy state of a grid cell. Binary-valued measurement vectors are the thresholded output of a sensor in a radar, sonar, or other sensory system. We compare the performance of the proposed framework to that of the classical formulation for occupancy grids. The results show that the proposed framework identifies occupancy grids with lower false alarm and miss detection rates, and requires fewer observations of the surrounding area, to generate an accurate estimate of occupancy probabilities when compared to conventional formulations.
Related Topics
Concepts
Occupancy
Occupancy grid mapping
Computer science
Grid
Bayesian probability
Disjoint sets
Binary number
Posterior probability
Independence (probability theory)
Algorithm
False alarm
Artificial intelligence
Mathematics
Statistics
Engineering
Architectural engineering
Robot
Arithmetic
Mobile robot
Geometry
Combinatorics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/tits.2020.3019813
- OA Status
- green
- References
- 27
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2990112790
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2990112790Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tits.2020.3019813Digital Object Identifier
- Title
-
Bayesian Learning of Occupancy GridsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-09-09Full publication date if available
- Authors
-
Christopher Robbiano, Edwin K. P. Chong, M.R. Azimi-Sadjadi, Louis L. Scharf, Ali PezeshkiList of authors in order
- Landing page
-
https://doi.org/10.1109/tits.2020.3019813Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1911.07915Direct OA link when available
- Concepts
-
Occupancy, Occupancy grid mapping, Computer science, Grid, Bayesian probability, Disjoint sets, Binary number, Posterior probability, Independence (probability theory), Algorithm, False alarm, Artificial intelligence, Mathematics, Statistics, Engineering, Architectural engineering, Robot, Arithmetic, Mobile robot, Geometry, CombinatoricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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