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MICP-L: Mesh-based ICP for Robot Localization using Hardware-Accelerated Ray Casting

Published 25 Oct 2022 in cs.RO | (2210.13904v4)

Abstract: Triangle mesh maps are a versatile 3D environment representation for robots to navigate in challenging indoor and outdoor environments exhibiting tunnels, hills and varying slopes. To make use of these mesh maps, methods are needed to accurately localize robots in such maps to perform essential tasks like path planning and navigation. We present Mesh ICP Localization (MICP-L), a novel and computationally efficient method for registering one or more range sensors to a triangle mesh map to continuously localize a robot in 6D, even in GPS-denied environments. We accelerate the computation of ray casting correspondences (RCC) between range sensors and mesh maps by supporting different parallel computing devices like multicore CPUs, GPUs and the latest NVIDIA RTX hardware. By additionally transforming the covariance computation into a reduction operation, we can optimize the initial guessed poses in parallel on CPUs or GPUs, making our implementation applicable in real-time on many architectures. We demonstrate the robustness of our localization approach with datasets from agricultural, aerial, and automotive domains.

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References (28)
  1. M. Eisoldt, M. Flottmann, J. Gaal, P. Buschermöhle, S. Hinderink, M. Hillmann, A. Nitschmann, P. Hoffmann, T. Wiemann, and M. Porrmann, “HATSDF SLAM – Hardware-accelerated TSDF SLAM for Reconfigurable SoCs,” in European Conference on Mobile Robots (ECMR).   IEEE, 2021, pp. 1–7.
  2. S. Pütz, T. Wiemann, M. Kleine Piening, and J. Hertzberg, “Continuous Shortest Path Vector Field Navigation on 3D Triangular Meshes for Mobile Robots,” in International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 2256–2263.
  3. A. Mock, T. Wiemann, and J. Hertzberg, “Rmagine: 3D Range Sensor Simulation in Polygonal Maps via Raytracing for Embedded Hardware on Mobile Robots,” in International Conference on Robotics and Automation (ICRA).   IEEE, 2023, pp. 9076–9082.
  4. J. Ruan, B. Li, Y. Wang, and Y. Sun, “SLAMesh: Real-time LiDAR Simultaneous Localization and Meshing,” in International Conference on Robotics and Automation (ICRA).   IEEE, 2023, pp. 3546–3552.
  5. T. Wiemann, I. Mitschke, A. Mock, and J. Hertzberg, “Surface Reconstruction from Arbitrarily Large Point Clouds,” in International Conference on Robotic Computing (IRC).   IEEE, 2018, pp. 278–281.
  6. A. Dai, M. Nießner, M. Zollhöfer, S. Izadi, and C. Theobalt, “BundleFusion: Real-Time Globally Consistent 3D Reconstruction Using On-the-Fly Surface Reintegration,” ACM Transactions on Graphics (ToG), vol. 36, no. 4, p. 1, 2017.
  7. I. Vizzo, X. Chen, N. Chebrolu, J. Behley, and C. Stachniss, “Poisson Surface Reconstruction for LiDAR Odometry and Mapping,” in International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 5624–5630.
  8. J. Lin, C. Yuan, Y. Cai, H. Li, Y. Zou, X. Hong, and F. Zhang, “ImMesh: An Immediate LiDAR Localization and Meshing Framework,” arXiv preprint arXiv:2301.05206, 2023.
  9. M. Dreher, H. Blum, R. Siegwart, and A. Gawel, “Global Localization in Meshes,” in International Symposium on Automation and Robotics in Construction (ISARC), vol. 38.   IAARC, 2021, pp. 747–754.
  10. X. Chen, I. Vizzo, T. Läbe, J. Behley, and C. Stachniss, “Range Image-based LiDAR Localization for Autonomous Vehicles,” in International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 5802–5808.
  11. S. Rusinkiewicz and M. Levoy, “Efficient Variants of the ICP Algorithm,” in International Conference on 3-D Digital Imaging and Modeling (3DIM).   IEEE, 2001, pp. 145–152.
  12. K. Koide, M. Yokozuka, S. Oishi, and A. Banno, “Voxelized GICP for Fast and Accurate 3D Point Cloud Registration,” in International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 11 054–11 059.
  13. T. Shan and B. Englot, “LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain,” in International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2018, pp. 4758–4765.
  14. T. Shan, B. Englot, D. Meyers, W. Wang, C. Ratti, and R. Daniela, “LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping,” in International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2020, pp. 5135–5142.
  15. A. Segal, D. Haehnel, and S. Thrun, “Generalized-ICP,” in Robotics: science and systems (RSS), 2009.
  16. D. Holz and S. Behnke, “Registration of Non-Uniform Density 3D Point Clouds using Approximate Surface Reconstruction,” in International Symposium on Robotics (ISR).   VDE, 2014, pp. 1–7.
  17. W. Li and P. Song, “A modified ICP algorithm based on dynamic adjustment factor for registration of point cloud and CAD model,” Pattern Recognition Letters, vol. 65, pp. 88–94, 2015.
  18. A. Avetisyan, M. Dahnert, A. Dai, M. Savva, A. X. Chang, and M. Nießner, “Scan2CAD: Learning CAD Model Alignment in RGB-D Scans,” in Conference on Computer Vision and Pattern Recognition (CVPR).   IEEE, 2019, pp. 2614–2623.
  19. D. Mejia-Parra, J. Lalinde-Pulido, J. R. Sánchez, O. Ruiz-Salguero, and J. Posada, “Perfect Spatial Hashing for Point-cloud-to-mesh Registration,” in CEIG.   The Eurographics Association, 2019, pp. 41–50.
  20. P. Bourquat, D. Coeurjolly, G. Damiand, and F. Dupont, “Hierarchical mesh-to-points as-rigid-as-possible registration,” Computers & Graphics, vol. 102, pp. 320–328, 2022.
  21. J. Zhang, Y. Yao, and B. Deng, “Fast and Robust Iterative Closest Point,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3450–3466, 2022.
  22. I. Wald, S. Woop, C. Benthin, G. S. Johnson, and M. Ernst, “Embree: A Kernel Framework for Efficient CPU Ray Tracing,” ACM Transactions on Graphics (TOG), 2014.
  23. S. G. Parker, J. Bigler, A. Dietrich, H. Friedrich, J. Hoberock, D. Luebke, D. McAllister, M. McGuire, K. Morley, A. Robison, and M. Stich, “OptiX: A General Purpose Ray Tracing Engine,” ACM Transactions on Graphics (TOG), 2010.
  24. S. Umeyama, “Least-squares estimation of transformation parameters between two point patterns,” IEEE Pattern Analysis & Machine Intelligence (PAMI), vol. 13, no. 04, pp. 376–380, 1991.
  25. S. Pütz, J. S. Simón, and J. Hertzberg, “Move Base Flex: A Highly Flexible Navigation Framework for Mobile Robots,” in International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2018, pp. 3416–3421.
  26. G. Kim, Y. S. Park, Y. Cho, J. Jeong, and A. Kim, “MulRan: Multimodal Range Dataset for Urban Place Recognition,” in International Conference on Robotics and Automation (ICRA).   IEEE, 2020, pp. 6246–6253.
  27. M. Helmberger, K. Morin, B. Berner, N. Kumar, G. Cioffi, and D. Scaramuzza, “The Hilti SLAM Challenge Dataset,” IEEE Robotics and Automation Letters (RAL), vol. 7, no. 3, pp. 7518–7525, 2022.
  28. S. Madgwick et al., “An efficient orientation filter for inertial and inertial/magnetic sensor arrays,” Report x-io and University of Bristol (UK), 2010.
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