Sparse Bayesian Inference for Dense Semantic Mapping
This addresses dense semantic mapping for robotics, offering incremental improvements through sparse Bayesian inference.
The paper tackles the high-dimensional inference challenge in dense robotic mapping by proposing a sparse Bayesian model based on relevance vector machines, which produces continuous probabilistic semantic maps with efficient queries. Results show promising improvements over state-of-the-art techniques on NYU Depth V2 and KITTI benchmark datasets.
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.