CoSLAM: collaborative visual SLAM in dynamic environments.

This paper studies the problem of vision-based simultaneous localization and mapping (SLAM) in dynamic environments with multiple cameras. These cameras move independently and can be mounted on different platforms. All cameras work together to build a global map, including 3D positions of static background points and trajectories of moving foreground points. We introduce intercamera pose estimation and intercamera mapping to deal with dynamic objects in the localization and mapping process. To further enhance the system robustness, we maintain the position uncertainty of each map point. To facilitate intercamera operations, we cluster cameras into groups according to their view overlap, and manage the split and merge of camera groups in real time. Experimental results demonstrate that our system can work robustly in highly dynamic environments and produce more accurate results in static environments.

Automated 3D trajectory measuring of large numbers of moving particles.

Complex dynamics of natural particle systems, such as insect swarms, bird flocks, fish schools, has attracted great attention of scientists for years. Measuring 3D trajectory of each individual in a group is vital for quantitative study of their dynamic properties, yet such empirical data is rare mainly due to the challenges of maintaining the identities of large numbers of individuals with similar visual features and frequent occlusions. We here present an automatic and efficient algorithm to track 3D motion trajectories of large numbers of moving particles using two video cameras. Our method solves this problem by formulating it as three linear assignment problems (LAP). For each video sequence, the first LAP obtains 2D tracks of moving targets and is able to maintain target identities in the presence of occlusions; the second one matches the visually similar targets across two views via a novel technique named maximum epipolar co-motion length (MECL), which is not only able to effectively reduce matching ambiguity but also further diminish the influence of frequent occlusions; the last one links 3D track segments into complete trajectories via computing a globally optimal assignment based on temporal and kinematic cues. Experiment results on simulated particle swarms with various particle densities validated the accuracy and robustness of the proposed method. As real-world case, our method successfully acquired 3D flight paths of fruit fly (Drosophila melanogaster) group comprising hundreds of freely flying individuals.