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arXiv · 2107.09265

Consensus-Informed Optimization Over Mixtures for Ambiguity-Aware Object SLAM

Ziqi Lu·Qiangqiang Huang·Kevin Doherty·John Leonard
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Abstract

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.

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Ziqi Lu, Qiangqiang Huang, Kevin Doherty, John Leonard. 2021-07-20. Consensus-Informed Optimization Over Mixtures for Ambiguity-Aware Object SLAM. https://arxiv.org/abs/2107.09265

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