Transitional Grid Maps: Joint Modeling of Static and Dynamic Occupancy
Autonomous agents rely on sensor data to construct representations of their environments, essential for predicting future events and planning their actions. However, sensor measurements suffer from limited range, occlusions, and sensor noise. These challenges become more evident in highly dynamic en...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10813430/ |
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| _version_ | 1832591973026889728 |
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| author | Jose Manuel Gaspar Sanchez Leonard Bruns Jana Tumova Patric Jensfelt Martin Torngren |
| author_facet | Jose Manuel Gaspar Sanchez Leonard Bruns Jana Tumova Patric Jensfelt Martin Torngren |
| author_sort | Jose Manuel Gaspar Sanchez |
| collection | DOAJ |
| description | Autonomous agents rely on sensor data to construct representations of their environments, essential for predicting future events and planning their actions. However, sensor measurements suffer from limited range, occlusions, and sensor noise. These challenges become more evident in highly dynamic environments. This work proposes a probabilistic framework to jointly infer which parts of an environment are statically and which parts are dynamically occupied. We formulate the problem as a Bayesian network and introduce minimal assumptions that significantly reduce the complexity of the problem. Based on those, we derive Transitional Grid Maps (TGMs), an efficient analytical solution. Using real data, we demonstrate how this approach produces better maps than the state-of-the-art by keeping track of both static and dynamic elements and, as a side effect, can help improve existing SLAM algorithms. |
| format | Article |
| id | doaj-art-10d743cacda74621bc6a61228a2bd940 |
| institution | Kabale University |
| issn | 2687-7813 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-10d743cacda74621bc6a61228a2bd9402025-01-22T00:00:23ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132025-01-01611010.1109/OJITS.2024.352144910813430Transitional Grid Maps: Joint Modeling of Static and Dynamic OccupancyJose Manuel Gaspar Sanchez0https://orcid.org/0000-0001-9982-578XLeonard Bruns1https://orcid.org/0000-0001-8747-6359Jana Tumova2https://orcid.org/0000-0003-4173-2593Patric Jensfelt3https://orcid.org/0000-0002-1170-7162Martin Torngren4https://orcid.org/0000-0002-4300-885XMechatronics Division, KTH Royal Institute of Technology, Stockholm, SwedenRobotics, Perception and Learning Division, KTH Royal Institute of Technology, Stockholm, SwedenRobotics, Perception and Learning Division, KTH Royal Institute of Technology, Stockholm, SwedenRobotics, Perception and Learning Division, KTH Royal Institute of Technology, Stockholm, SwedenMechatronics Division, KTH Royal Institute of Technology, Stockholm, SwedenAutonomous agents rely on sensor data to construct representations of their environments, essential for predicting future events and planning their actions. However, sensor measurements suffer from limited range, occlusions, and sensor noise. These challenges become more evident in highly dynamic environments. This work proposes a probabilistic framework to jointly infer which parts of an environment are statically and which parts are dynamically occupied. We formulate the problem as a Bayesian network and introduce minimal assumptions that significantly reduce the complexity of the problem. Based on those, we derive Transitional Grid Maps (TGMs), an efficient analytical solution. Using real data, we demonstrate how this approach produces better maps than the state-of-the-art by keeping track of both static and dynamic elements and, as a side effect, can help improve existing SLAM algorithms.https://ieeexplore.ieee.org/document/10813430/Grid mapBayesian inferenceSLAM |
| spellingShingle | Jose Manuel Gaspar Sanchez Leonard Bruns Jana Tumova Patric Jensfelt Martin Torngren Transitional Grid Maps: Joint Modeling of Static and Dynamic Occupancy IEEE Open Journal of Intelligent Transportation Systems Grid map Bayesian inference SLAM |
| title | Transitional Grid Maps: Joint Modeling of Static and Dynamic Occupancy |
| title_full | Transitional Grid Maps: Joint Modeling of Static and Dynamic Occupancy |
| title_fullStr | Transitional Grid Maps: Joint Modeling of Static and Dynamic Occupancy |
| title_full_unstemmed | Transitional Grid Maps: Joint Modeling of Static and Dynamic Occupancy |
| title_short | Transitional Grid Maps: Joint Modeling of Static and Dynamic Occupancy |
| title_sort | transitional grid maps joint modeling of static and dynamic occupancy |
| topic | Grid map Bayesian inference SLAM |
| url | https://ieeexplore.ieee.org/document/10813430/ |
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