This work presents a novel  approach to simultaneously track a robot with respect to multiple rigid entities, including the environment and additional dynamic objects in a scene . Previous approaches treat dynamic parts of a scene as outliers and are thus limited to  small amount of dynamics , or rely on prior information for all objects in the scene to enable robust camera tracking. Here, we propose to formulate localisation and object tracking as the same underlying problem and simultaneously track multiple rigid transformations, thereforeenabling simultaneous localisation and object tracking for mobile manipulators in dynamic scenes . We evaluate our approach on multiple challenging dynamic scenes with large occlusions . The evaluation demonstrates that our approach achieves better scene segmentationand camera pose tracking in highly dynamic scenes without requiring knowledge of the dynamic object's  appearance.
This work presents a novel RGB-D SLAM approach to simultaneously segment, track and reconstruct the static background and large dynamic rigid objects that can occlude major portions of the camera view . Previous approaches treat dynamic parts of a scene as outliers and are thus limited to a small amount of changes in the scene , or rely on prior information for all objects in the scene to enable robust camera tracking. Here, we propose to treat all dynamic parts as one rigid body and simultaneously segment and track both static and dynamic components. We, therefore, enable simultaneous localisation and reconstruction of both the static background and rigid dynamic components in environments where dynamic objects cause large occlusion . We evaluate our approach on multiple challenging scenes with large dynamic occlusion . The evaluation demonstrates that our approach achieves better motion segmentation, localisation and mapping without requiring prior knowledge of the dynamic object's shape and appearance.
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RGB-D SLAM
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track a robot with respect to multiple rigid entities, including the environment and additional dynamic objects in a scene
segment, track and reconstruct the static background and large dynamic rigid objects that can occlude major portions of the camera view
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a
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dynamics
changes in the scene
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formulate localisation and object tracking as the same underlying problem and simultaneously track multiple rigid transformations, thereforeenabling
treat all dynamic parts as one rigid body and simultaneously segment and track both static and dynamic components. We, therefore, enable
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object tracking for mobile manipulators in dynamic scenes
reconstruction of both the static background and rigid dynamic components in environments where dynamic objects cause large occlusion
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dynamic
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occlusions
dynamic occlusion
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scene segmentationand camera pose tracking in highly dynamic scenes without requiring
motion segmentation, localisation and mapping without requiring prior
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shape and