ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings

Abstract

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.

arXiv comment2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

References in corpus (8)

• Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
• DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments
• Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects
• Robust Dense Mapping for Large-Scale Dynamic Environments
• Track to Reconstruct and Reconstruct to Track
• Multimotion Visual Odometry (MVO): Simultaneous Estimation of Camera and Third-Party Motions
• RGB-D SLAM in Dynamic Environments Using Point Correlations
• Spatiotemporal Articulated Models for Dynamic SLAM