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Found 5 papers, 3 papers with code
DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM
We present in this paper DynaSLAM II, a visual SLAM system for stereo and RGB-D configurations that tightly integrates the multi-object tracking capability.
Empty Cities: a Dynamic-Object-Invariant Space for Visual SLAM
The first challenge is addressed by the use of a convolutional network that learns a multi-class semantic segmentation of the image.
Empty Cities: Image Inpainting for a Dynamic-Object-Invariant Space
In this paper we present an end-to-end deep learning framework to turn images that show dynamic content, such as vehicles or pedestrians, into realistic static frames.
Dynamic Objects Segmentation for Visual Localization in Urban Environments
Yet, in highly dynamic environments, like crowded city streets, problems arise as major parts of the image can be covered by dynamic objects.
DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
And it also estimates a map of the static parts of the scene, which is a must for long-term applications in real-world environments.
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