TY - GEN
T1 - Variational filtering with copula models for SLAM
AU - Martin, John D.
AU - Doherty, Kevin
AU - Cyr, Caralyn
AU - Englot, Brendan
AU - Leonard, John
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/24
Y1 - 2020/10/24
N2 - The ability to infer map variables and estimate pose is crucial to the operation of autonomous mobile robots. In most cases the shared dependency between these variables is modeled through a multivariate Gaussian distribution, but there are many situations where that assumption is unrealistic. Our paper shows how it is possible to relax this assumption and perform simultaneous localization and mapping (SLAM) with a larger class of distributions, whose multivariate dependency is represented with a copula model. We integrate the distribution model with copulas into a Sequential Monte Carlo estimator and show how unknown model parameters can be learned through gradient-based optimization. We demonstrate our approach is effective in settings where Gaussian assumptions are clearly violated, such as environments with uncertain data association and nonlinear transition models.
AB - The ability to infer map variables and estimate pose is crucial to the operation of autonomous mobile robots. In most cases the shared dependency between these variables is modeled through a multivariate Gaussian distribution, but there are many situations where that assumption is unrealistic. Our paper shows how it is possible to relax this assumption and perform simultaneous localization and mapping (SLAM) with a larger class of distributions, whose multivariate dependency is represented with a copula model. We integrate the distribution model with copulas into a Sequential Monte Carlo estimator and show how unknown model parameters can be learned through gradient-based optimization. We demonstrate our approach is effective in settings where Gaussian assumptions are clearly violated, such as environments with uncertain data association and nonlinear transition models.
UR - http://www.scopus.com/inward/record.url?scp=85102406954&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102406954&partnerID=8YFLogxK
U2 - 10.1109/IROS45743.2020.9341404
DO - 10.1109/IROS45743.2020.9341404
M3 - Conference contribution
AN - SCOPUS:85102406954
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 5066
EP - 5073
BT - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
T2 - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
Y2 - 24 October 2020 through 24 January 2021
ER -