Sparse Bayesian Inference for Dense Semantic Mapping

TL;DR

This paper introduces a novel dense semantic mapping method using sparse Bayesian models, enabling efficient, probabilistic, high-resolution queries in robotic mapping tasks.

Contribution

It applies relevance vector machines for dense semantic mapping, offering a sparse, probabilistic, and computationally efficient alternative to existing dense mapping techniques.

Findings

Improved mapping accuracy over state-of-the-art methods

Efficient high-resolution queries at any scale

Probabilistic semantic outputs for better uncertainty handling

Abstract

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.