A Variational Feature Encoding Method of 3D Object for Probabilistic Semantic SLAM
This addresses the challenge of view-independent loop closure and semantic mapping for mobile robots, representing an incremental improvement in probabilistic modeling for 3D object recognition.
The paper tackles the problem of probabilistic observation modeling for 3D objects in semantic SLAM by approximating complex distributions with a variational auto-encoder, enabling Bayesian inference and achieving results in classification and shape retrieval tasks.
This paper presents a feature encoding method of complex 3D objects for high-level semantic features. Recent approaches to object recognition methods become important for semantic simultaneous localization and mapping (SLAM). However, there is a lack of consideration of the probabilistic observation model for 3D objects, as the shape of a 3D object basically follows a complex probability distribution. Furthermore, since the mobile robot equipped with a range sensor observes only a single view, much information of the object shape is discarded. These limitations are the major obstacles to semantic SLAM and view-independent loop closure using 3D object shapes as features. In order to enable the numerical analysis for the Bayesian inference, we approximate the true observation model of 3D objects to tractable distributions. Since the observation likelihood can be obtained from the generative model, we formulate the true generative model for 3D object with the Bayesian networks. To capture these complex distributions, we apply a variational auto-encoder. To analyze the approximated distributions and encoded features, we perform classification with maximum likelihood estimation and shape retrieval.