Collaborative Multi-vehicle SLAM with moving object tracking
Although simultaneous localization and mapping (SLAM) algorithms are widely appreciated in mobile robot navigation, they can be further improved to suit practical applications in dynamic environmental conditions. One such important improvement is the detection and tracking of moving objects present...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
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IEEE
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/5964 |
| _version_ | 1848744943109013504 |
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| author | Moratuwage, D. Vo, Ba-Ngu Wang, D |
| author2 | N/A |
| author_facet | N/A Moratuwage, D. Vo, Ba-Ngu Wang, D |
| author_sort | Moratuwage, D. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Although simultaneous localization and mapping (SLAM) algorithms are widely appreciated in mobile robot navigation, they can be further improved to suit practical applications in dynamic environmental conditions. One such important improvement is the detection and tracking of moving objects present in the sensor field of view (FOV). In this paper we propose to extend our recently introduced CollaborativeMulti-vehicle SLAM (CMSLAM) solution based on the random finite set (RFS) representation of the feature map and measurements, by tracking both static and dynamic features. We represent static features observed during the SLAM process, along with dynamic features present in the current sensor FOV, as an augmented RFS. The corresponding probability density is propagated using a Bayes recursion, from which the static feature map and the estimates of dynamic feature locations can be obtained. Measurement update in the CMSLAM process is carried out only using the static feature map to take advantage of obvious accuracy improvements. |
| first_indexed | 2025-11-14T06:09:29Z |
| format | Conference Paper |
| id | curtin-20.500.11937-5964 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:09:29Z |
| publishDate | 2013 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-59642017-09-13T14:42:16Z Collaborative Multi-vehicle SLAM with moving object tracking Moratuwage, D. Vo, Ba-Ngu Wang, D N/A Although simultaneous localization and mapping (SLAM) algorithms are widely appreciated in mobile robot navigation, they can be further improved to suit practical applications in dynamic environmental conditions. One such important improvement is the detection and tracking of moving objects present in the sensor field of view (FOV). In this paper we propose to extend our recently introduced CollaborativeMulti-vehicle SLAM (CMSLAM) solution based on the random finite set (RFS) representation of the feature map and measurements, by tracking both static and dynamic features. We represent static features observed during the SLAM process, along with dynamic features present in the current sensor FOV, as an augmented RFS. The corresponding probability density is propagated using a Bayes recursion, from which the static feature map and the estimates of dynamic feature locations can be obtained. Measurement update in the CMSLAM process is carried out only using the static feature map to take advantage of obvious accuracy improvements. 2013 Conference Paper http://hdl.handle.net/20.500.11937/5964 10.1109/ICRA.2013.6631397 IEEE restricted |
| spellingShingle | Moratuwage, D. Vo, Ba-Ngu Wang, D Collaborative Multi-vehicle SLAM with moving object tracking |
| title | Collaborative Multi-vehicle SLAM with moving object tracking |
| title_full | Collaborative Multi-vehicle SLAM with moving object tracking |
| title_fullStr | Collaborative Multi-vehicle SLAM with moving object tracking |
| title_full_unstemmed | Collaborative Multi-vehicle SLAM with moving object tracking |
| title_short | Collaborative Multi-vehicle SLAM with moving object tracking |
| title_sort | collaborative multi-vehicle slam with moving object tracking |
| url | http://hdl.handle.net/20.500.11937/5964 |