IMLS-SLAM: scan-to-model matching based on 3D data

Abstract : The Simultaneous Localization And Mapping (SLAM) problem has been well studied in the robotics community, especially using mono, stereo cameras or depth sensors. 3D depth sensors, such as Velodyne LiDAR, have proved in the last 10 years to be very useful to perceive the environment in autonomous driving, but few methods exist that directly use these 3D data for odometry. We present a new low-drift SLAM algorithm based only on 3D LiDAR data. Our method relies on a scan-to-model matching framework. We first have a specific sampling strategy based on the LiDAR scans. We then define our model as the previous localized LiDAR sweeps and use the Implicit Moving Least Squares (IMLS) surface representation. We show experiments with the Velodyne HDL32 with only 0.40% drift over a 4 km acquisition without any loop closure (i.e., 16 m drift after 4 km). We tested our solution on the KITTI benchmark with a Velodyne HDL64 and ranked among the best methods (against mono, stereo and LiDAR methods) with a global drift of only 0.69%.
Type de document :
Communication dans un congrès
ICRA - International Conference on Robotics and Automation, May 2018, Brisbane, Australia. 2018 IEEE International Conference on Robotics and Automation (ICRA)
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https://hal.archives-ouvertes.fr/hal-01959570
Contributeur : Jean-Emmanuel Deschaud <>
Soumis le : lundi 14 janvier 2019 - 16:14:52
Dernière modification le : jeudi 7 février 2019 - 15:36:35

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  • HAL Id : hal-01959570, version 1

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Jean-Emmanuel Deschaud. IMLS-SLAM: scan-to-model matching based on 3D data. ICRA - International Conference on Robotics and Automation, May 2018, Brisbane, Australia. 2018 IEEE International Conference on Robotics and Automation (ICRA). 〈hal-01959570〉

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