Spatio SLAM: An Integrated Strategy for Robot Autonomy in Variable Indoor Luminance
Perceiving and understanding dynamic scenes by a robot vision system requires crucial spatio-temporal knowledge and geometry to enable long-term autonomy. Existing Dynamic SLAM (Simultaneous Localization And Mapping) addresses definite portions of robot pose estimation to make it accurate. On the co...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11112625/ |
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| author | Rapti Chaudhuri Suman Deb Abhijit Das |
| author_facet | Rapti Chaudhuri Suman Deb Abhijit Das |
| author_sort | Rapti Chaudhuri |
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| description | Perceiving and understanding dynamic scenes by a robot vision system requires crucial spatio-temporal knowledge and geometry to enable long-term autonomy. Existing Dynamic SLAM (Simultaneous Localization And Mapping) addresses definite portions of robot pose estimation to make it accurate. On the contrary scene understanding and rational integration with position is hard to find. In this context, a comprehensive realisation of a variable scene shared with other dynamic agents is still a challenge to execute within a shifting drift. This challenge is even increased when the environment is with variable luminance. To address these challenges, we propose a spatial-geometric SLAM (Spatio SLAM) solution that unifies the exiting connotations of long and short-term dynamics that constructs a real-time dense spatial-geometric map. The geometric representation of the scene gives the autonomous agent a prior map to factorize the navigation framework. In this work an end-to-end DA-SSOD (Domain Adaptive Single Stage Object Detection) deep network is introduced into the object detection module. This DA-SSOD introduction experimentally confirms the accurate prediction of obstacles in complex indoor surroundings with variable luminance. Geometric maps built by Spatio SLAM (as the real-time reconstruction of a 3D scene over an instance), results in 86.6%precision in static and 87.4%precision in varying environment (with dynamic obstacles). SOTA (State Of The Art) analysis presents the superiority of Spatio SLAM on other SLAM techniques implemented across multiple indoor structures. |
| format | Article |
| id | doaj-art-e8ca0b79d95f4ced8d61185f40c53a45 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-e8ca0b79d95f4ced8d61185f40c53a452025-08-20T03:40:43ZengIEEEIEEE Access2169-35362025-01-011313689413690810.1109/ACCESS.2025.359557211112625Spatio SLAM: An Integrated Strategy for Robot Autonomy in Variable Indoor LuminanceRapti Chaudhuri0https://orcid.org/0000-0001-5732-2391Suman Deb1https://orcid.org/0000-0002-1457-4713Abhijit Das2https://orcid.org/0000-0001-5796-0748CSE Department, National Institute of Technology Agartala, Agartala, IndiaCSE Department, National Institute of Technology Agartala, Agartala, IndiaManipal Academy of Higher Education, Manipal Institute of Technology Bengaluru, Manipal, IndiaPerceiving and understanding dynamic scenes by a robot vision system requires crucial spatio-temporal knowledge and geometry to enable long-term autonomy. Existing Dynamic SLAM (Simultaneous Localization And Mapping) addresses definite portions of robot pose estimation to make it accurate. On the contrary scene understanding and rational integration with position is hard to find. In this context, a comprehensive realisation of a variable scene shared with other dynamic agents is still a challenge to execute within a shifting drift. This challenge is even increased when the environment is with variable luminance. To address these challenges, we propose a spatial-geometric SLAM (Spatio SLAM) solution that unifies the exiting connotations of long and short-term dynamics that constructs a real-time dense spatial-geometric map. The geometric representation of the scene gives the autonomous agent a prior map to factorize the navigation framework. In this work an end-to-end DA-SSOD (Domain Adaptive Single Stage Object Detection) deep network is introduced into the object detection module. This DA-SSOD introduction experimentally confirms the accurate prediction of obstacles in complex indoor surroundings with variable luminance. Geometric maps built by Spatio SLAM (as the real-time reconstruction of a 3D scene over an instance), results in 86.6%precision in static and 87.4%precision in varying environment (with dynamic obstacles). SOTA (State Of The Art) analysis presents the superiority of Spatio SLAM on other SLAM techniques implemented across multiple indoor structures.https://ieeexplore.ieee.org/document/11112625/Spatio SLAMspatial-geometric mapdynamic SLAMspatio-temporal knowledgedrift shift |
| spellingShingle | Rapti Chaudhuri Suman Deb Abhijit Das Spatio SLAM: An Integrated Strategy for Robot Autonomy in Variable Indoor Luminance IEEE Access Spatio SLAM spatial-geometric map dynamic SLAM spatio-temporal knowledge drift shift |
| title | Spatio SLAM: An Integrated Strategy for Robot Autonomy in Variable Indoor Luminance |
| title_full | Spatio SLAM: An Integrated Strategy for Robot Autonomy in Variable Indoor Luminance |
| title_fullStr | Spatio SLAM: An Integrated Strategy for Robot Autonomy in Variable Indoor Luminance |
| title_full_unstemmed | Spatio SLAM: An Integrated Strategy for Robot Autonomy in Variable Indoor Luminance |
| title_short | Spatio SLAM: An Integrated Strategy for Robot Autonomy in Variable Indoor Luminance |
| title_sort | spatio slam an integrated strategy for robot autonomy in variable indoor luminance |
| topic | Spatio SLAM spatial-geometric map dynamic SLAM spatio-temporal knowledge drift shift |
| url | https://ieeexplore.ieee.org/document/11112625/ |
| work_keys_str_mv | AT raptichaudhuri spatioslamanintegratedstrategyforrobotautonomyinvariableindoorluminance AT sumandeb spatioslamanintegratedstrategyforrobotautonomyinvariableindoorluminance AT abhijitdas spatioslamanintegratedstrategyforrobotautonomyinvariableindoorluminance |