FIELD: navigation of mobile robots for mobile applications.
SUBSTANCE: method performs scene understanding and object recognition. The method is proposed in which a point cloud is taken, reconstructed or obtained from depth sensors. First, this point cloud is represented as a voxel representation. The voxel representation is then processed with a 3D sparse convolutional "skeleton". Next, a 3D sparse convolution "neck" is used to extract features at different levels of the "skeleton". The resulting features are processed through a 3D sparse convolutional "head" with weights common to different feature levels. At each feature level, the "head" is responsible for predicting objects of a certain scale. The "head" gives the bounding box options for each position. Finally, all predictions received are filtered to select the most likely ones.
EFFECT: increasing the accuracy of object detection and reducing the scene processing time.
3 cl, 4 dwg, 4 tbl
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Authors
Dates
2023-03-10—Published
2022-05-18—Filed