In this letter , we present an active visual SLAM approach for omnidirectional robots. The goal is to generate control commands that allow such a robot to simultaneously localize itself and map an unknown environment while maximizing the amount of information gained and consume as little energy as possible. Leveraging the robot's independent translation and rotation control, we introduce a multi-layered approach for active V-SLAM. The top layer decides on informative goal locations and generates highly informative paths to them. The second and third layers actively re-plan and execute the path, exploiting the continuously updated map  . Moreover, they allow the robot to maximize its visibility of 3D visual features in the environment . Through rigorous simulations, real robot experiments and comparisons with the state-of-the-art methods, we demonstrate that our approach achieves similar coverage and lesser overall map entropy  while keeping the traversed distance up to 36\% less than the other methods  . Code and implementation details are provided as open-source .
In this paper , we present an active visual SLAM approach for omnidirectional robots. The goal is to generate control commands that allow such a robot to simultaneously localize itself and map an unknown environment while maximizing the amount of information gained and consume as low energy as possible. Leveraging the robot's independent translation and rotation control, we introduce a multi-layered approach for active V-SLAM. The top layer decides on informative goal locations and generates highly informative paths to them. The second and third layers actively re-plan and execute the path, exploiting the continuously updated map and local features information . Moreover, we introduce two utility formulations to account for the obstacle presence in the field of view and the robot's location . Through rigorous simulations, real robot experiments and comparisons with the state-of-the-art methods, we demonstrate that our approach achieves similar coverage results with lesser overall map entropy . This is obtained while keeping the traversed distance up to 39\% shorter than the other methods and without increasing the wheels' total rotation amount . Code and implementation details are provided as opensource .
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letter
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little
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and local features information
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they allow the robot to maximize its visibility of 3D visual features in the environment
we introduce two utility formulations to account for the obstacle presence in the field of view and the robot's location
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. This is obtained
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36\% less
39\% shorter
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and without increasing the wheels' total rotation amount
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open-source
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