Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/105814
Title: Multi-channel convolutional neural network based 3D object detection for indoor robot environmental perception
Authors: Wang, Li
Li, Ruifeng
Shi, Hezi
Sun, Jingwen
Zhao, Lijun
Tandianus, Budianto
Seah, Hock Soon
Quah, Chee Kwang
Keywords: Multi-Channel CNN
3D Object Detection
DRNTU::Engineering::Computer science and engineering
Issue Date: 2019
Source: Wang, L., Li, R., Shi, H., Sun, J., Zhao, L., Seah, H. S., . . . Tandianus, B. (2019). Multi-channel convolutional neural network based 3D object detection for indoor robot environmental perception. Sensors, 19(4), 893-. doi:10.3390/s19040893
Series/Report no.: Sensors
Abstract: 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.
URI: https://hdl.handle.net/10356/105814
http://hdl.handle.net/10220/48782
ISSN: 1424-8220
DOI: 10.3390/s19040893
Rights: © 2019 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
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