  Despite impressive advances in simultaneous localization and mapping, dense
robotic mapping remains challenging due to its inherent nature of being a
high-dimensional inference problem. In this paper, we propose a dense semantic
robotic mapping technique that exploits sparse Bayesian models, in particular,
the relevance vector machine, for high-dimensional sequential inference. The
technique is based on the principle of automatic relevance determination and
produces sparse models that use a small subset of the original dense training
set as the dominant basis. The resulting map posterior is continuous, and
queries can be made efficiently at any resolution. Moreover, the technique has
probabilistic outputs per semantic class through Bayesian inference. We
evaluate the proposed relevance vector semantic map using publicly available
benchmark datasets, NYU Depth V2 and KITTI; and the results show promising
improvements over the state-of-the-art techniques.
