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.

We propose a novel 6 DoF visual SLAM method based on the structural regularity of man-made building environments. The idea is that we use the building structure lines as features for localization and mapping. Unlike other line features, the building structure lines encode the global orientation information that constrains the heading of the camera over time, eliminating the accumulated orientation errors and reducing the position drift in consequence. We extend the standard EKF visual SLAM method to adopt the building structure lines with a novel parametrization method that represents the structure lines in dominant directions. Experiments have been conducted in both synthetic and real-world scenes. The results show that, our method performs remarkably better than the existing methods in terms of position error and orientation error. In the test of indoor scenes of the public RAWSEEDS datasets, with the aid of wheel odometer, our method produces bounded position errors about 0.79 meter along a 967 meter path even though no loop closing algorithm is applied.

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In China, the incidence of CP is rising rapidly; alcohol and biliary stones are the main causes. Endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography are highly sensitive and specific diagnostic methods.