Multi-Channel Convolutional Neural Network Based 3D Object Detection for Indoor Robot Environmental Perception

Sensors
Li WangBudianto Tandianus

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.

References

Jul 17, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·Pedro F FelzenszwalbDeva Ramanan
Apr 14, 2011·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Markus Enzweiler, Dariu M Gavrila
Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Kaiming HeJian Sun
Jun 14, 2016·IEEE Transactions on Pattern Analysis and Machine Intelligence·Shaoqing RenJian Sun
Jul 15, 2017·IEEE Transactions on Cybernetics·Alejandro GonzalezJaume Amores

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Methods Mentioned

BETA
feature extraction

Software Mentioned

Fast R CNN
VoxelNet
AVOD
R
PointNet
Frustum
MV3D
Faster R - CNN
3DVP
GoogLeNet

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