Visual perception and navigation of security robot based on deep learning

This work presents a vision based security robot perception and control strategy for semi-structured and unstructured roads navigation. The main contributions contain deep learning technique for road recognition and a hybrid navigation scheme. A deep convolutional neural network is employed to perform pixel-wise segmentation and thus to find road regions. Secondly, based on the segmented regions, an edge extraction algorithm is designed to extract and fit the road boundaries. To ensure the robustness of navigation, the region detection algorithm is proposed to ensure the robot to movement on the traversable area. Experimental results verify the effectiveness of proposed visual navigation approaches.

[1]  Xiangyu Zhang,et al.  ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[2]  Eduardo Romera,et al.  ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation , 2018, IEEE Transactions on Intelligent Transportation Systems.

[3]  Keyu Lu,et al.  A hierarchical approach for road detection , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[4]  Eugenio Culurciello,et al.  ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation , 2016, ArXiv.

[5]  Hongbin Zha,et al.  Scene-Adaptive Off-Road Detection Using a Monocular Camera , 2018, IEEE Transactions on Intelligent Transportation Systems.

[6]  Xiaojing Wang,et al.  Curve path detection of unstructured roads for the outdoor robot navigation , 2013, Math. Comput. Model..

[7]  Sebastian Thrun,et al.  Self-supervised Monocular Road Detection in Desert Terrain , 2006, Robotics: Science and Systems.

[8]  Laurent Itti,et al.  Mobile robot navigation system in outdoor pedestrian environment using vision-based road recognition , 2013, 2013 IEEE International Conference on Robotics and Automation.

[9]  Shengyan Zhou,et al.  Self-supervised learning method for unstructured road detection using Fuzzy Support Vector Machines , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Yu Wang,et al.  Lednet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation , 2019, 2019 IEEE International Conference on Image Processing (ICIP).

[11]  Linda G. Shapiro,et al.  ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation , 2018, ECCV.

[12]  Sebastian Ramos,et al.  The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Yong Liu,et al.  Traversable region detection with a learning framework , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[14]  Franz Kummert,et al.  Spatial ray features for real-time ego-lane extraction , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[15]  Xiaojuan Qi,et al.  ICNet for Real-Time Semantic Segmentation on High-Resolution Images , 2017, ECCV.

[16]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Chao He,et al.  Vision-Based Real-Time Traversable Region Detection for Mobile Robot in the Outdoors , 2017, Sensors.

[18]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Keyu Lu,et al.  Vision Sensor-Based Road Detection for Field Robot Navigation , 2015, Sensors.

[20]  Laurent Itti,et al.  Mobile robot monocular vision navigation based on road region and boundary estimation , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  Sheng Tang,et al.  CGNet: A Light-Weight Context Guided Network for Semantic Segmentation , 2018, IEEE Transactions on Image Processing.

[22]  Andreas Zell,et al.  Long range traversable region detection based on superpixels clustering for mobile robots , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[23]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Yong Li,et al.  Road detection algorithm for Autonomous Navigation Systems based on dark channel prior and vanishing point in complex road scenes , 2016, Robotics Auton. Syst..

[25]  Leonidas J. Guibas,et al.  Situational Fusion of Visual Representation for Visual Navigation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[26]  R. Makwana,et al.  Computationally efficient vanishing point detection algorithm based road segmentation in road images , 2016, 2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT).

[27]  Jean Ponce,et al.  General Road Detection From a Single Image , 2010, IEEE Transactions on Image Processing.