Visual-LiDAR SLAM with loop closure

State-of-the-art LIDAR odometry techniques are exceptionally precise. However, while they solve the localization problem, they perform mapping on-the-run, not being able to close loops, neither re- ...

[1]  Michael Bosse,et al.  Zebedee: Design of a Spring-Mounted 3-D Range Sensor with Application to Mobile Mapping , 2012, IEEE Transactions on Robotics.

[2]  Ji Zhang,et al.  Visual-lidar odometry and mapping: low-drift, robust, and fast , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[3]  Simon Lacroix,et al.  ICP-based pose-graph SLAM , 2016, 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

[4]  Ji Zhang,et al.  On degeneracy of optimization-based state estimation problems , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[5]  Dorian Gálvez-López,et al.  Bags of Binary Words for Fast Place Recognition in Image Sequences , 2012, IEEE Transactions on Robotics.

[6]  Ji Zhang,et al.  Enabling aggressive motion estimation at low-drift and accurate mapping in real-time , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[7]  Sridha Sridharan,et al.  Probabilistic Surfel Fusion for Dense LiDAR Mapping , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[8]  Michael Himmelsbach,et al.  Fast segmentation of 3D point clouds for ground vehicles , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[9]  Wolfgang Hess,et al.  Real-time loop closure in 2D LIDAR SLAM , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[10]  Michael Bosse,et al.  Continuous‐Time Three‐Dimensional Mapping for Micro Aerial Vehicles with a Passively Actuated Rotating Laser Scanner , 2016, J. Field Robotics.

[11]  Roland Siegwart,et al.  A Review of Point Cloud Registration Algorithms for Mobile Robotics , 2015, Found. Trends Robotics.

[12]  Soohwan Kim,et al.  Complementary Perception for Handheld SLAM , 2018, IEEE Robotics and Automation Letters.

[13]  W. Burgard,et al.  RAWSEEDS: Robotics Advancement through Web-publishing of Sensorial and Elaborated Extensive Data Sets , 2010 .

[14]  Wolfram Burgard,et al.  A benchmark for the evaluation of RGB-D SLAM systems , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Cyrill Stachniss,et al.  Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments , 2018, Robotics: Science and Systems.

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

[17]  Michael Bosse,et al.  Continuous 3D scan-matching with a spinning 2D laser , 2009, 2009 IEEE International Conference on Robotics and Automation.

[18]  Adrian Haarbach.  Continuous time trajectory estimation for 3 D SLAM from an actuated 2 D laser scanner , 2016 .

[19]  Volker Grabe,et al.  Compact , Real-time Localization without Reliance on Infrastructure , 2016 .

[20]  Ji Zhang,et al.  LOAM: Lidar Odometry and Mapping in Real-time , 2014, Robotics: Science and Systems.

[21]  Bertrand Douillard,et al.  On the segmentation of 3D LIDAR point clouds , 2011, 2011 IEEE International Conference on Robotics and Automation.

[22]  Jun Miura,et al.  Large-scale 3D outdoor mapping and on-line localization using 3D-2D matching , 2017, 2017 IEEE/SICE International Symposium on System Integration (SII).

[23]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..

[24]  Renaud Dubé,et al.  SegMatch: Segment based place recognition in 3D point clouds , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[25]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Weikun Zhen,et al.  Robust localization and localizability estimation with a rotating laser scanner , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[27]  John J. Leonard,et al.  Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age , 2016, IEEE Transactions on Robotics.

[28]  R. Siegwart,et al.  Non-uniform sampling strategies for continuous correction based trajectory estimation , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[29]  Sridha Sridharan,et al.  Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[30]  Ji Zhang,et al.  Real-time depth enhanced monocular odometry , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[31]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Sven Behnke,et al.  Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[33]  Jörg Stückler,et al.  Multi-resolution surfel mapping and real-time pose tracking using a continuously rotating 2D laser scanner , 2013, 2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).