论文标题
clustervo：集群移动实例并估算自我和周围环境
ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings
论文作者
论文摘要
我们提出了ClusterVo，这是一种立体视觉探光仪，同时簇和估计了自我和周围刚性簇/对象的运动。与以前的解决方案依靠批处理输入或在场景结构或动态对象模型上强加于先验的解决方案不同，Clustervo在线，一般，因此可以在各种场景中使用，包括室内场景的理解和自动驾驶。我们系统的核心是多级概率关联机制和异构条件随机场（CRF）聚类方法，将语义，空间和运动信息结合在一起，以共同在线推断群集分段。相机和动态对象的姿势通过滑动窗口优化立即解决。我们的系统在牛津多动力和Kitti数据集上进行了定量和定性的评估，从而与探针和动态轨迹恢复的最先进的解决方案达到了可比的结果。
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.
