A Research on the Fusion of Semantic Segment Network and SLAM

The development of traditional SLAM technology has gradually encountered bottlenecks in recent years. With deep learning, especially vision-based machine learning, a new and improved way of development has been found for SLAM named Semantic SLAM. The study of semantic SLAM has been a very hot topic in recent years. However, the related research is still in its infancy and is not systematic. In this paper, we will discuss the existing SLAM systems, semantic segmentation networks and semantic SLAM systems, introduce secondly the multiple effects of semantic network on localization, mapping and their applications, and finally put forward an idea of semantic Fusion.

[1]  Sean L. Bowman,et al.  Probabilistic data association for semantic SLAM , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Silvio Savarese,et al.  SEGCloud: Semantic Segmentation of 3D Point Clouds , 2017, 2017 International Conference on 3D Vision (3DV).

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

[4]  Andrew W. Fitzgibbon,et al.  KinectFusion: Real-time dense surface mapping and tracking , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.

[5]  Dieter Fox,et al.  DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks , 2017, Robotics: Science and Systems.

[6]  Roland Siegwart,et al.  Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback , 2017, Int. J. Robotics Res..

[7]  Juan D. Tardós,et al.  ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras , 2016, IEEE Transactions on Robotics.

[8]  Roberto Cipolla,et al.  Segmentation and Recognition Using Structure from Motion Point Clouds , 2008, ECCV.

[9]  Stefan Leutenegger,et al.  ElasticFusion: Dense SLAM Without A Pose Graph , 2015, Robotics: Science and Systems.

[10]  Daniel Cremers,et al.  LSD-SLAM: Large-Scale Direct Monocular SLAM , 2014, ECCV.

[11]  Jürgen Sturm,et al.  Evaluating Egomotion and Structure-from-Motion Approaches Using the TUM RGB-D Benchmark , 2012 .

[12]  Alexandre Boulch,et al.  SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks , 2017, Comput. Graph..

[13]  Thomas Brox,et al.  DeMoN: Depth and Motion Network for Learning Monocular Stereo , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Davide Scaramuzza,et al.  SVO: Fast semi-direct monocular visual odometry , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[15]  Paul H. J. Kelly,et al.  SLAM++: Simultaneous Localisation and Mapping at the Level of Objects , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Stefan Leutenegger,et al.  SemanticFusion: Dense 3D semantic mapping with convolutional neural networks , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[17]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Shu Liu,et al.  Associatively Segmenting Instances and Semantics in Point Clouds , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Wolfram Burgard,et al.  Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters , 2007, IEEE Transactions on Robotics.

[20]  Roberto Cipolla,et al.  PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[21]  Sen Wang,et al.  DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[22]  J. M. M. Montiel,et al.  ORB-SLAM: A Versatile and Accurate Monocular SLAM System , 2015, IEEE Transactions on Robotics.

[23]  Jianxiong Xiao,et al.  SUN RGB-D: A RGB-D scene understanding benchmark suite , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).