Journal
SENSORS
Volume 19, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/s19040893
Keywords
3D object detection; multi-channel CNN; indoor robot; environmental perception
Funding
- National Natural Science Foundation of China [61673136, 61473103]
- Self-Planned Task of State Key Laboratory of Robotics and System (HIT) [SKLRS201715A, SKLRS201609B]
- Foundation for Innovative Research Groups of the National Natural Science Foundation of China [51521003]
- ST Engineering-NTU Corporate Lab through the NRF corporate lab @ university scheme
- China Scholarship Council [201706120137]
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Environmental perception is a vital feature for service robots when working in an indoor environment for a long time. The general 3D reconstruction is a low-level geometric information description that cannot convey semantics. In contrast, higher level perception similar to humans requires more abstract concepts, such as objects and scenes. Moreover, the 2D object detection based on images always fails to provide the actual position and size of an object, which is quite important for a robot's operation. In this paper, we focus on the 3D object detection to regress the object's category, 3D size, and spatial position through a convolutional neural network (CNN). We propose a multi-channel CNN for 3D object detection, which fuses three input channels including RGB, depth, and bird's eye view (BEV) images. We also propose a method to generate 3D proposals based on 2D ones in the RGB image and semantic prior. Training and test are conducted on the modified NYU V2 dataset and SUN RGB-D dataset in order to verify the effectiveness of the algorithm. We also carry out the actual experiments in a service robot to utilize the proposed 3D object detection method to enhance the environmental perception of the robot.
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