Skip to content

Robust Non-Gaussian Semantic Simultaneous Localization and Mapping

Kevin Doherty, S.M., 2019
John Leonard, Advisor
The recent success of object detection systems motivates object-based representations for robot navigation; i.e. semantic simultaneous localization and mapping (SLAM), in which we aim to jointly estimate a robot’s pose and both the location and semantic class of observed objects. We specifically consider semantic SLAM under non-Gaussian uncertainty, which often arises from data association uncertainty, where we do not know what objects in the environment caused the measurement made by our sensor. The semantic class of an object can inform data association; a detection classified as a door is unlikely to be associated to a chair object. However, detectors are imperfect, and incorrect classification of objects can be detrimental to data association.
The key insight we leverage is that the semantic SLAM problem with unknown data association can be reframed as a non-Gaussian inference problem. We present two solutions to the resulting problem: we first assume Gaussian measurement models, and non-Gaussianity only due to data association uncertainty. We then relax this assumption and provide a method that can cope with arbitrary non-Gaussian measurement models. We show quantitatively on both simulated and real data that both proposed methods have robustness advantages as compared to traditional solutions.