ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings
ClusterVO流程 ClusterVO将经过同步和校准的立体图像作为输入，输出相机和运动物体的位姿。 对每一帧使用YOLO进行目标检测，并提取ORB特征。为了对运动物体进行跟踪，本文首先通过多层概率关联将边界框和特征分别关联到聚类和地标点。 然后，本文对所有关联到地标点的特征点执行条件随机场（CRF），以确定当前帧的聚类分割。 最后使用运动先验和边缘化对滑动窗口上的所有状态进行位姿估计。 多层
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相关文章
- 1. D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry
- 2. VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry 语义视觉定位与里程计
- 3. 【IROS2018】Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots
- 4. MultiGrain: a unified image embedding for classes and instances
- 5. 多视角聚类（一）Multi-view clustering on data with partial instances and clusters
- 6. FaceNet: A Unified Embedding for Face Recognition and Clustering
- 7. Moving to “Micro-service” and “Serverless”
- 8. self-attention and transformer
- 9. Understand, to be understood, and keep moving
- 10. VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry 辅助学习 && 2018 论文笔记