  Building object-level maps can facilitate robot-environment interactions
(e.g. planning and manipulation), but objects could often have multiple
probable poses when viewed from a single vantage point, due to symmetry,
occlusion or perceptual failures. A robust object-level simultaneous
localization and mapping (object SLAM) algorithm needs to be aware of this pose
ambiguity. We propose to maintain and subsequently disambiguate the multiple
pose interpretations to gradually recover a globally consistent world
representation. The max-mixtures model is applied to implicitly and efficiently
track all pose hypotheses, but the resulting formulation is non-convex, and
therefore subject to local optima. To mitigate this problem, temporally
consistent hypotheses are extracted, guiding the optimization into the global
optimum. This consensus-informed inference method is applied online via
landmark variable re-initialization within an incremental SLAM framework,
iSAM2, for robust real-time performance. We demonstrate that this approach
improves SLAM performance on both simulated and real object SLAM problems with
pose ambiguity.
