Micro aerial vehicles usually have strap-down sensors on the vehicle body, leading to the severe coupling effect between perception and trajectory planning. As a result, visual-inertial simultaneous localization and mapping (VI-SLAM) technologies implemented on MAVs suffer from tracking failure problems, especially in featureless environments. To overcome these challenges, based on MAVs with movable camera mechanisms (e.g., gimbal stabilizer, pan-tilt, or bionic neck-eye system), we proposed two sampling-based algorithms for known and unknown environments respectively. The first active perception planning algorithm based on a scene richness model is developed with a built feature map for the environment. Differ from the first algorithm, the second one is modified for active localization in unknown 3D space. It is basically a time-based sampling-based approach that uses the same scene richness model. In addition, it also achieved a balance between exploitation and exploration. With the above solutions, the robustness of visual perception is improved while avoiding over-exploitation of known information. Simulation and real-world experiments are performed to verify the feasibility of our algorithms.
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Sampling-Based View Planning for MAVs in Active Visual-inertial State Estimation
Semantic Scholar · Engineering · 2022
Abstract
Micro aerial vehicles usually have strap-down sensors on the vehicle body, leading to the severe coupling effect between perception and trajectory planning. As a result, visual-inertial simultaneous localization and mapping (VI-SLAM) technologies implemented on MAVs suffer from tracking failure problems, especially in featureless environments. To overcome these challenges, based on MAVs with movable camera mechanisms (e.g., gimbal stabilizer, pan-tilt, or bionic neck-eye system), we proposed two sampling-based algorithms for known and unknown environments respectively. The first active perception planning algorithm based on a scene richness model is developed with a built feature map for the environment. Differ from the first algorithm, the second one is modified for active localization in unknown 3D space. It is basically a time-based sampling-based approach that uses the same scene richness model. In addition, it also achieved a balance between exploitation and exploration. With the above solutions, the robustness of visual perception is improved while avoiding over-exploitation of known information. Simulation and real-world experiments are performed to verify the feasibility of our algorithms.