Collaborative multi-vehicle localization and mapping in high clutter environments
Among today's robotics applications, exploration missions in dynamic, high clutter and uncertain environmental conditions is quite common. Autonomous multi-vehicle systems come in handy for such exploration missions since a team of autonomous vehicles can explore an environment more efficiently...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2013
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| Online Access: | http://hdl.handle.net/1721.1/79055 |
| _version_ | 1826190231915724800 |
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| author | Moratuwage, M. D. P. Wijesoma, W. S. Kalyan, Bharath Patrikalakis, Nicholas M. Moghadam, Peyman |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Moratuwage, M. D. P. Wijesoma, W. S. Kalyan, Bharath Patrikalakis, Nicholas M. Moghadam, Peyman |
| author_sort | Moratuwage, M. D. P. |
| collection | MIT |
| description | Among today's robotics applications, exploration missions in dynamic, high clutter and uncertain environmental conditions is quite common. Autonomous multi-vehicle systems come in handy for such exploration missions since a team of autonomous vehicles can explore an environment more efficiently and reliably than a single autonomous vehicle (AV). In order to improve the navigation accuracy, especially in the absence of a priori feature maps, various simultaneous localization and mapping (SLAM) algorithms are widely used in such applications. As for multi-vehicle scenarios, collaborative multi-vehicle simultaneous localization and mapping algorithm (CSLAM) is an effective strategy. However use of multiple AVs poses additional scaling problems such as inter-vehicle map fusion, and data association which needs to be addressed. Although existing CSLAM algorithms are shown to perform quite adequately in simulations, their performance is much less to be desired in high clutter scenarios that is inevitable in actual environments. In this paper, we present an approach to improve the performance of a CSLAM algorithm in the presence of high clutter, by combining an effective clutter filter framework based on Random Finite Sets (RFS). The performance of the improved CSLAM algorithm is evaluated using simulations under varying clutter conditions. |
| first_indexed | 2024-09-23T08:36:47Z |
| format | Article |
| id | mit-1721.1/79055 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:36:47Z |
| publishDate | 2013 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/790552022-09-23T13:17:40Z Collaborative multi-vehicle localization and mapping in high clutter environments Moratuwage, M. D. P. Wijesoma, W. S. Kalyan, Bharath Patrikalakis, Nicholas M. Moghadam, Peyman Massachusetts Institute of Technology. Department of Mechanical Engineering Patrikalakis, Nicholas M. Among today's robotics applications, exploration missions in dynamic, high clutter and uncertain environmental conditions is quite common. Autonomous multi-vehicle systems come in handy for such exploration missions since a team of autonomous vehicles can explore an environment more efficiently and reliably than a single autonomous vehicle (AV). In order to improve the navigation accuracy, especially in the absence of a priori feature maps, various simultaneous localization and mapping (SLAM) algorithms are widely used in such applications. As for multi-vehicle scenarios, collaborative multi-vehicle simultaneous localization and mapping algorithm (CSLAM) is an effective strategy. However use of multiple AVs poses additional scaling problems such as inter-vehicle map fusion, and data association which needs to be addressed. Although existing CSLAM algorithms are shown to perform quite adequately in simulations, their performance is much less to be desired in high clutter scenarios that is inevitable in actual environments. In this paper, we present an approach to improve the performance of a CSLAM algorithm in the presence of high clutter, by combining an effective clutter filter framework based on Random Finite Sets (RFS). The performance of the improved CSLAM algorithm is evaluated using simulations under varying clutter conditions. National Science Foundation (U.S.) Singapore–MIT Alliance for Research and Technology (SMART) Singapore-MIT Alliance for Research and Technology. Center for Environmental Sensing and Monitoring 2013-05-31T16:54:03Z 2013-05-31T16:54:03Z 2010-12 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-7814-9 9781424478132 1424478138 1424478154 INSPEC Accession Number: 11805799 http://hdl.handle.net/1721.1/79055 Moratuwage, M. D. P., W. S. Wijesoma, B. Kalyan, Nicholas M. Patrikalakis, and Peyman Moghadam. Collaborative Multi-vehicle Localization and Mapping in High Clutter Environments. In 2010 11th International Conference on Control Automation Robotics & Vision, Singapore, 7-10th December 2010, pp.1422-1427. © Copyright 2010 IEEE. en_US http://dx.doi.org/10.1109/ICARCV.2010.5707778 2010 11th International Conference on Control Automation Robotics & Vision (ICARCV) Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Moratuwage, M. D. P. Wijesoma, W. S. Kalyan, Bharath Patrikalakis, Nicholas M. Moghadam, Peyman Collaborative multi-vehicle localization and mapping in high clutter environments |
| title | Collaborative multi-vehicle localization and mapping in high clutter environments |
| title_full | Collaborative multi-vehicle localization and mapping in high clutter environments |
| title_fullStr | Collaborative multi-vehicle localization and mapping in high clutter environments |
| title_full_unstemmed | Collaborative multi-vehicle localization and mapping in high clutter environments |
| title_short | Collaborative multi-vehicle localization and mapping in high clutter environments |
| title_sort | collaborative multi vehicle localization and mapping in high clutter environments |
| url | http://hdl.handle.net/1721.1/79055 |
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