A Point-Line Feature based Visual SLAM Method in Dynamic Indoor Scene

Traditional visual SLAM methods employ point features to implement motion estimation and environment map construction. However, in some low-texture indoor scenarios, such as office and corridor, less reliable point features may be found, which could jeopardize the SLAM solution. In addition, scene changes among sequential images are not only caused by the position changes of image acquisition, but also by pedestrians and other moving objects in an indoor dynamic environment. Thus, feature identification for moving objects is needed as an important part for practical application of indoor SLAM. This paper proposes a point-line feature based SLAM method that combines both of points and line segments to enhance the performance of feature extraction in indoor scene, which can extract many line features from walls, furniture and other artificial objects. In this method, added line features help to gain more robust and accurate results. Additionally, a real-time object detection algorithm is introduced to identify the features extracted from pedestrians, so that to eliminate the negative effects caused by moving objects. The experimental results demonstrates that the proposed method can obtain more robust and accurate localization results in dynamic indoor scene.

[1]  Danping Zou,et al.  CoSLAM: Collaborative Visual SLAM in Dynamic Environments , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Luis Miguel Bergasa,et al.  On combining visual SLAM and dense scene flow to increase the robustness of localization and mapping in dynamic environments , 2012, 2012 IEEE International Conference on Robotics and Automation.

[3]  José M. Cañas,et al.  LineSLAM: Visual Real Time Localization Using Lines and UKF , 2013, ROBOT.

[4]  Olivier Stasse,et al.  MonoSLAM: Real-Time Single Camera SLAM , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Ignacio Parra,et al.  Robust visual odometry for vehicle localization in urban environments , 2009, Robotica.

[6]  Danping Zou,et al.  StructSLAM: Visual SLAM With Building Structure Lines , 2015, IEEE Transactions on Vehicular Technology.

[7]  Kaichang Di,et al.  Photogrammetric processing of rover imagery of the 2003 Mars Exploration Rover mission , 2008 .

[8]  Li Li,et al.  Hierarchical line matching based on Line-Junction-Line structure descriptor and local homography estimation , 2016, Neurocomputing.

[9]  Kurt Konolige,et al.  CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching , 2008, ECCV.

[10]  Il Hong Suh,et al.  Building a 3-D Line-Based Map Using Stereo SLAM , 2015, IEEE Transactions on Robotics.

[11]  Larry H. Matthies,et al.  Visual odometry on the Mars Exploration Rovers , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[12]  Simon Lacroix,et al.  Monocular-vision based SLAM using Line Segments , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[13]  Hans P. Moravec Obstacle avoidance and navigation in the real world by a seeing robot rover , 1980 .

[14]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[15]  Larry Matthies,et al.  Two years of Visual Odometry on the Mars Exploration Rovers: Field Reports , 2007 .

[16]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

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

[18]  Wolfram Burgard,et al.  An evaluation of the RGB-D SLAM system , 2012, 2012 IEEE International Conference on Robotics and Automation.

[19]  G. Klein,et al.  Parallel Tracking and Mapping for Small AR Workspaces , 2007, 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality.

[20]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, CVPR 2004.

[21]  Rafael Grompone von Gioi,et al.  LSD: A Fast Line Segment Detector with a False Detection Control , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.