| dc.contributor |
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
|
| dc.contributor |
Woods Hole Oceanographic Institution |
|
| dc.contributor |
Massachusetts Institute of Technology. Department of Mechanical Engineering |
|
| dc.creator |
Fourie, Dehann |
|
| dc.creator |
Rypkema, Nicholas R |
|
| dc.creator |
Teixeira, Pedro Vaz |
|
| dc.creator |
Claassens, Sam |
|
| dc.creator |
Fischell, Erin |
|
| dc.creator |
Leonard, John |
|
| dc.date |
2022-01-07T19:55:34Z |
|
| dc.date |
2022-01-07T19:55:34Z |
|
| dc.date |
2020 |
|
| dc.date |
2022-01-07T19:50:47Z |
|
| dc.date.accessioned |
2023-03-01T18:04:59Z |
|
| dc.date.available |
2023-03-01T18:04:59Z |
|
| dc.identifier |
https://hdl.handle.net/1721.1/138848 |
|
| dc.identifier |
Fourie, Dehann, Rypkema, Nicholas R, Teixeira, Pedro Vaz, Claassens, Sam, Fischell, Erin et al. 2020. "Towards Real-Time Non-Gaussian SLAM for Underdetermined Navigation." IEEE International Conference on Intelligent Robots and Systems. |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/278678 |
|
| dc.description |
© 2020 IEEE. This paper presents a method for processing sparse, non-Gaussian multimodal data in a simultaneous localization and mapping (SLAM) framework using factor graphs. Our approach demonstrates the feasibility of using a sum-product inference strategy to recover functional belief marginals from highly non-Gaussian situations, relaxing the prolific unimodal Gaussian assumption. The method is more focused than conventional multi-hypothesis approaches, but still captures dominant modes via multi-modality. The proposed algorithm exists in a trade space that spans the anticipated uncertainty of measurement data, task-specific performance, sensor quality, and computational cost. This work leverages several major algorithm design constructs, including clique recycling, to put an upper bound on the allowable computational expense - a major challenge in non-parametric methods. To better demonstrate robustness, experimental results show the feasibility of the method on at least two of four major sources of non-Gaussian behavior: i) the first introduces a canonical range-only problem which is always underdetermined although composed exclusively from Gaussian measurements; ii) a real-world AUV dataset, demonstrating how ambiguous acoustic correlator measurements are directly incorporated into a non-Gaussian SLAM solution, while using dead reckon tethering to overcome short term computational requirements. |
|
| dc.format |
application/pdf |
|
| dc.language |
en |
|
| dc.publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
|
| dc.relation |
10.1109/IROS45743.2020.9341490 |
|
| dc.relation |
IEEE International Conference on Intelligent Robots and Systems |
|
| dc.rights |
Creative Commons Attribution-Noncommercial-Share Alike |
|
| dc.rights |
http://creativecommons.org/licenses/by-nc-sa/4.0/ |
|
| dc.source |
MIT web domain |
|
| dc.title |
Towards Real-Time Non-Gaussian SLAM for Underdetermined Navigation |
|
| dc.type |
Article |
|
| dc.type |
http://purl.org/eprint/type/ConferencePaper |
|