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Despite impressive advances in simultaneous localization and mapping, denserobotic mapping remains challenging due to its inherent nature of being ahigh-dimensional inference problem. In this paper, we propose a dense semanticrobotic mapping technique that exploits sparse Bayesian models, in particular,the relevance vector machine, for high-dimensional sequential inference. Thetechnique is based on the principle of automatic relevance determination andproduces sparse models that use a small subset of the original dense trainingset as the dominant basis. The resulting map posterior is continuous, andqueries can be made efficiently at any resolution. Moreover, the technique hasprobabilistic outputs per semantic class through Bayesian inference. Weevaluate the proposed relevance vector semantic map using publicly availablebenchmark datasets, NYU Depth V2 and KITTI; and the results show promisingimprovements over the state-of-the-art techniques.

Sparse Bayesian Inference for Dense Semantic Mapping