Abstract: A fundamental task for mobile robots is simultaneous localization and mapping (SLAM). Moreover, long-term robustness is an important property for SLAM. When vehicles or robots steer fast or steer in certain scenarios, such as low-texture environments, long corridors, tunnels, or other duplicated structural environments, most SLAM systems might fail. In this paper, we propose a novel robust visual inertial light detection and ranging (LiDaR) navigation (VILN) SLAM system, including stereo visual-inertial LiDaR odometry and visual-LiDaR loop closure. The proposed VILN SLAM system can perform well with low drift after long-term experiments, even when the LiDaR or visual measurements are degraded occasionally in complex scenes. Extensive experimental results show that the robustness has been greatly improved in various scenarios compared to state-of-the-art SLAM systems.

用于长期自主机器人的新型鲁棒同时定位与建图方法

[1]Banerjee N, Connolly RC, Lisin D, et al., 2019. View management for lifelong visual maps. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.7871-7878. doi: 10.1109/IROS40897.2019.8968245

[2]Davison AJ, Reid ID, Molton ND, et al., 2007. MonoSLAM: real-time single camera SLAM. IEEE Trans Patt Anal Mach Intell, 29(6):1052-1067. doi: 10.1109/TPAMI.2007.1049

[3]Deschaud JE, 2018. IMLS-SLAM: scan-to-model matching based on 3D data. IEEE Int Conf on Robotics and Automation, p.2480-2485. doi: 10.1109/ICRA.2018.8460653

[4]Engel J, Schöps T, Cremers D, 2014. LSD-SLAM: large-scale direct monocular SLAM. Proc 13^th European Conf on Computer Vision, p.834-849. doi: 10.1007/978-3-319-10605-2_54

[5]Engel J, Koltun V, Cremers D, 2018. Direct sparse odometry. IEEE Trans Patt Anal Mach Intell, 40(3):611-625. doi: 10.1109/TPAMI.2017.2658577

[6]Forster C, Pizzoli M, Scaramuzza D, 2014. SVO: fast semi-direct monocular visual odometry. IEEE Int Conf on Robotics and Automation, p.15-22. doi: 10.1109/ICRA.2014.6906584

[7]Forster C, Carlone L, Dellaert F, et al., 2017. On-manifold preintegration for real-time visual-inertial odometry. IEEE Trans Robot, 33(1):1-21. doi: 10.1109/TRO.2016.2597321

[8]Geiger A, Lenz P, Stiller C, et al., 2013. Vision meets robotics: the KITTI dataset. Int J Robot Res, 32(11):1231-1237. doi: 10.1177/0278364913491297

[9]Grisetti G, Stachniss C, Burgard W, 2007. Improved techniques for grid mapping with Rao-Blackwellized particle filters. IEEE Trans Robot, 23(1):34-46. doi: 10.1109/TRO.2006.889486

[10]Hemann G, Singh S, Kaess M, 2016. Long-range GPS-denied aerial inertial navigation with lidar localization. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.1659-1666. doi: 10.1109/IROS.2016.7759267

[11]Hess W, Kohler D, Rapp H, et al., 2016. Real-time loop closure in 2D LIDAR SLAM. IEEE Int Conf on Robotics and Automation, p.1271-1278. doi: 10.1109/ICRA.2016.7487258

[12]Kerl C, Sturm J, Cremers D, 2013. Dense visual SLAM for RGB-D cameras. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.2100-2106. doi: 10.1109/IROS.2013.6696650

[13]Kim G, Park B, Kim A, 2019. 1-day learning, 1-year localization: long-term LiDAR localization using scan context image. IEEE Robot Autom Lett, 4(2):1948-1955. doi: 10.1109/LRA.2019.2897340

[14]Klein G, Murray D, 2007. Parallel tracking and mapping for small AR workspaces. Proc 6^th IEEE and ACM Int Symp on Mixed and Augmented Reality, p.1-10. doi: 10.1109/ISMAR.2007.4538852

[15]Konolige K, Grisetti G, Kümmerle R, et al., 2010. Efficient sparse pose adjustment for 2D mapping. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.22-29. doi: 10.1109/IROS.2010.5649043

[16]Lee J, Hwang S, Lee K, et al., 2020. AD-VO: scale-resilient visual odometry using attentive disparity map. https://arxiv.org/abs/2001.02090

[17]Maurer CR, Qi RS, Raghavan V, 2003. A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions. IEEE Trans Patt Anal Mach Intell, 25(2):265-270. doi: 10.1109/TPAMI.2003.1177156

[18]Mur-Artal R, Tardós JD, 2017. ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Trans Robot, 33(5):1255-1262. doi: 10.1109/TRO.2017.2705103

[19]Mur-Artal R, Montiel JMM, Tardós JD, 2015. ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans Robot, 31(5):1147-1163. doi: 10.1109/TRO.2015.2463671

[20]Nair GB, Daga S, Sajnani R, et al., 2020. Multi-object monocular SLAM for dynamic environments. https://arxiv.org/abs/2002.03528

[21]Newcombe RA, Lovegrove SJ, Davison AJ, 2011. DTAM: dense tracking and mapping in real-time. Int Conf on Computer Vision, p.2320-2327. doi: 10.1109/ICCV.2011.6126513

[22]Patel N, Khorrami F, Krishnamurthy P, et al., 2019. Tightly coupled semantic RGB-D inertial odometry for accurate long-term localization and mapping. Proc 19^th Int Conf on Advanced Robotics, p.523-528. doi: 10.1109/ICAR46387.2019.8981658

[23]Rusinkiewicz S, Levoy M, 2001. Efficient variants of the ICP algorithm. Proc 3^rd Int Conf on 3-D Digital Imaging and Modeling, p.145-152. doi: 10.1109/IM.2001.924423

[24]Shao WZ, Vijayarangan S, Li C, et al., 2019. Stereo visual inertial LiDAR simultaneous localization and mapping. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.370-377. doi: 10.1109/IROS40897.2019.8968012

[25]Sibley G, Matthies L, Sukhatme G, 2010. Sliding window filter with application to planetary landing. J Field Robot, 27(5):587-608. doi: 10.1002/rob.20360

[26]Sünderhauf N, Neubert P, Protzel P, 2013. Predicting the change—a step towards life-long operation in everyday environments. Proc Challenges and Vision Workshop at RSS, p.1-4.

[27]Wagstaff B, Peretroukhin V, Kelly J, 2020. Self-supervised deep pose corrections for robust visual odometry. IEEE Int Conf on Robotics and Automation, p.2331-2337. doi: 10.1109/ICRA40945.2020.9197562

[28]Wang ZJ, Wu Y, Niu QQ, 2020. Multi-sensor fusion in automated driving: a survey. IEEE Access, 8:2847-2868. doi: 10.1109/ACCESS.2019.2962554

[29]Xu YL, Ou YS, Xu TT, 2018. SLAM of robot based on the fusion of vision and LIDAR. IEEE Int Conf on Cyborg and Bionic Systems, p.121-126. doi: 10.1109/CBS.2018.8612212

[30]Zhang J, Singh S, 2015. Visual-lidar odometry and mapping: low-drift, robust, and fast. IEEE Int Conf on Robotics and Automation, p.2174-2181. doi: 10.1109/ICRA.2015.7139486

[31]Zhao HJ, Chiba M, Shibasaki R, et al., 2008. SLAM in a dynamic large outdoor environment using a laser scanner. IEEE Int Conf on Robotics and Automation, p.1455-1462. doi: 10.1109/ROBOT.2008.4543407

[32]Zhao ZR, Mao YJ, Ding Y, et al., 2019. Visual-based semantic SLAM with landmarks for large-scale outdoor environment. Proc 2^nd China Symp on Cognitive Computing and Hybrid Intelligence, p.149-154. doi: 10.1109/CCHI.2019.8901910

[33]Zhu XR, Qiu CX, Deng FC, et al., 2017. Cloud-based real-time outsourcing localization for a ground mobile robot in large-scale outdoor environments. J Field Robot, 34(7):1313-1331. doi: 10.1002/rob.21712