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SLICT: Multi-Input Multi-Scale Surfel-Based Lidar-Inertial Continuous-Time Odometry and Mapping
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0003-4815-9689
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-1170-7162
Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore..
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2023 (English)In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 8, no 4, p. 2102-2109Article in journal (Refereed) Published
Abstract [en]

While feature association to a global map has significant benefits, to keep the computations from growing exponentially, most lidar-based odometry and mapping methods opt to associate features with local maps at one voxel scale. Taking advantage of the fact that surfels (surface elements) at different voxel scales can be organized in a tree-like structure, we propose an octree-based global map of multi-scale surfels that can be updated incrementally. This alleviates the need for recalculating, for example, a k-d tree of the whole map repeatedly. The system can also take input from a single or a number of sensors, reinforcing the robustness in degenerate cases. We also propose a point-to-surfel (PTS) association scheme, continuous-time optimization on PTS and IMU preintegration factors, along with loop closure and bundle adjustment, making a complete framework for Lidar-Inertial continuous-time odometry and mapping. Experiments on public and in-house datasets demonstrate the advantages of our system compared to other state-of-the-art methods.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2023. Vol. 8, no 4, p. 2102-2109
Keywords [en]
Laser radar, Feature extraction, Optimization, Costs, Robot kinematics, Source coding, Octrees, Localization, mapping, sensor fusion
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-325223DOI: 10.1109/LRA.2023.3246390ISI: 000942347900010Scopus ID: 2-s2.0-85149417556OAI: oai:DiVA.org:kth-325223DiVA, id: diva2:1748382
Note

QC 20230403

Available from: 2023-04-03 Created: 2023-04-03 Last updated: 2024-01-17Bibliographically approved

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Nguyen, Thien-MinhDuberg, DanielJensfelt, Patric

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