  In object-based Simultaneous Localization and Mapping (SLAM), 6D object poses
offer a compact representation of landmark geometry useful for downstream
planning and manipulation tasks. However, measurement ambiguity then arises as
objects may possess complete or partial object shape symmetries (e.g., due to
occlusion), making it difficult or impossible to generate a single consistent
object pose estimate. One idea is to generate multiple pose candidates to
counteract measurement ambiguity. In this paper, we develop a novel approach
that enables an object-based SLAM system to reason about multiple pose
hypotheses for an object, and synthesize this locally ambiguous information
into a globally consistent robot and landmark pose estimation formulation. In
particular, we (1) present a learned pose estimation network that provides
multiple hypotheses about the 6D pose of an object; (2) by treating the output
of our network as components of a mixture model, we incorporate pose
predictions into a SLAM system, which, over successive observations, recovers a
globally consistent set of robot and object (landmark) pose estimates. We
evaluate our approach on the popular YCB-Video Dataset and a simulated video
featuring YCB objects. Experiments demonstrate that our approach is effective
in improving the robustness of object-based SLAM in the face of object pose
ambiguity.
