Open AccessIndoor Semantic Mapping with Efficient Convolutional Neural Networks for Resource-constrained SLAM System
Author(s) -
Jiahao Zhang,
Zhiqiang Wang,
Qing Zhu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1544/1/012136
Subject(s) - computer science , artificial intelligence , pooling , encoder , pyramid (geometry) , computation , parsing , convolution (computer science) , segmentation , convolutional neural network , mobile robot , deep learning , computer vision , robot , pattern recognition (psychology) , artificial neural network , algorithm , physics , optics , operating system
Scene parsing and robotic manipulation using visual sensing are of significant importance for mobile and robotics related applications. And increasing efforts are being made to associate semantic concepts to geometric entities in robot’s surroundings. However, they are not well suitable for resource-constrained and mobile platforms. Toward an efficient semantic mapping solution, we propose a specifically light-weight segmentation network for building a dense 3D semantic map in real-time. The propose architecture is based on depth-wise convolution and channel shuffle to reduce computation cost. An improved Atrous Spatial Pyramid Pooling (ASPP) and encoder-decoder structures are presented and multi-scale features are merged for better prediction. Experiments are compared with other methods to prove the effectiveness and superiority.