動的物体クラスタリングと自己・周囲の視覚オドメトリ推定を同時に行うステレオVO
ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings
ステレオ画像からカメラ自身と周囲の動く物体をオンラインでクラスタリングし、それぞれの動きを同時に推定する視覚オドメトリ手法を提案した論文。
著者: Jiahui Huang, Sheng Yang, Tai-Jiang Mu, Shi-Min Hu
分類: cs.CV, cs.RO
原文アブストラクト
We present ClusterVO, a stereo Visual Odometry which simultaneously clusters and estimates the motion of both ego and surrounding rigid clusters/objects. Unlike previous solutions relying on batch input or imposing priors on scene structure or dynamic object models, ClusterVO is online, general and thus can be used in various scenarios including indoor scene understanding and autonomous driving. At the core of our system lies a multi-level probabilistic association mechanism and a heterogeneous Conditional Random Field (CRF) clustering approach combining semantic, spatial and motion information to jointly infer cluster segmentations online for every frame. The poses of camera and dynamic objects are instantly solved through a sliding-window optimization. Our system is evaluated on Oxford Multimotion and KITTI dataset both quantitatively and qualitatively, reaching comparable results to state-of-the-art solutions on both odometry and dynamic trajectory recovery.
