Name
Abstract | ||
|---|---|---|
We study the ambiguous data association problem confronting simultaneous localization and mapping (SLAM), specifically for the autonomous exploration of environments lacking rich features. In such environments, a single false positive assignment might lead to catastrophic failure, which even robust back-ends may be unable to resolve. Inspired by multiple hypothesis tracking, we present a novel approach to effectively manage multiple hypotheses (MH) of data association inherited from traditional joint compatibility branch and bound (JCBB), which entails the generation, ordering and elimination of hypotheses. We analyze the performance of MHJCBB in two particular situations, one applying it to SLAM over a predefined trajectory and the other showing its applicability in exploring unknown environments. Statistical results demonstrate that MHJCBB's maintenance of diverse hypotheses under ambiguous conditions significantly improves map accuracy. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IROS.2018.8593753 | 2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
Field | DocType | ISSN |
Computer vision,Noise measurement,Computer science,Joint compatibility branch and bound,Multiple hypotheses,Measurement uncertainty,Catastrophic failure,Data association,Artificial intelligence,Simultaneous localization and mapping,Machine learning,Trajectory | Conference | 2153-0858 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinkun Wang | 1 | 7 | 5.91 |
| Brendan Englot | 2 | 221 | 21.53 |