Multimodal Semantic SLAM with Probabilistic Data Association
Name
doherty_icra2019_revised.pdf
Description
Accepted version
Size
1.96 MB
Format
Unknown
Checksum (MD5)
be91c3ab2e0e75ff9adf201df924eabe
Author(s)
Doherty, Kevin
Fourie, Dehann
Leonard, John
Date Issued
May 2019
Journal
Proceedings - IEEE International Conference on Robotics and Automation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Doherty, Kevin, Fourie, Dehann and Leonard, John. 2019. "Multimodal Semantic SLAM with Probabilistic Data Association." Proceedings - IEEE International Conference on Robotics and Automation, 2019-May.
Version
Author's final manuscript
Abstract
© 2019 IEEE. The recent success of object detection systems motivates object-based representations for robot navigation; i.e. semantic simultaneous localization and mapping (SLAM). The semantic SLAM problem can be decomposed into a discrete inference problem: determining object class labels and measurement-landmark correspondences (the data association problem), and a continuous inference problem: obtaining the set of robot poses and object locations in the environment. A solution to the semantic SLAM problem necessarily addresses this joint inference, but under ambiguous data associations this is in general a non-Gaussian inference problem, while the majority of previous work focuses on Gaussian inference. Previous solutions to data association either produce solutions between potential hypotheses or maintain multiple explicit hypotheses for each association. We propose a solution that represents hypotheses as multiple modes of an equivalent non-Gaussian sensor model. We then solve the resulting non-Gaussian inference problem using nonparametric belief propagation. We validate our approach in a simulated hallway environment under a variety of sensor noise characteristics, as well as using real data from the KITTI dataset, demonstrating improved robustness to perceptual aliasing and odometry uncertainty.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA.2019.8794244
