3D物体の変分観測モデルによる確率的意味SLAM
A Variational Observation Model of 3D Object for Probabilistic Semantic SLAM
RGB単眼カメラで見える物体の単一視点から、3D形状の観測モデルを扱いやすい分布で近似し、物体の姿勢と特徴をベイズ推定する意味SLAM手法を提案した。追加のループ検出器なしで自動ループ閉じ込めが機能することを示した。
分類: cs.RO, cs.CV
原文アブストラクト
We present a Bayesian object observation model for complete probabilistic semantic SLAM. Recent studies on object detection and feature extraction have become important for scene understanding and 3D mapping. However, 3D shape of the object is too complex to formulate the probabilistic observation model; therefore, performing the Bayesian inference of the object-oriented features as well as their pose is less considered. Besides, when the robot equipped with an RGB mono camera only observes the projected single view of an object, a significant amount of the 3D shape information is abandoned. Due to these limitations, semantic SLAM and viewpoint-independent loop closure using volumetric 3D object shape is challenging. In order to enable the complete formulation of probabilistic semantic SLAM, we approximate the observation model of a 3D object with a tractable distribution. We also estimate the variational likelihood from the 2D image of the object to exploit its observed single view. In order to evaluate the proposed method, we perform pose and feature estimation, and demonstrate that the automatic loop closure works seamlessly without additional loop detector in various environments.
