RF-LIO: Removal-First Tightly-coupled Lidar Inertial Odometry in High Dynamic Environments
Simultaneous Localization and Mapping (SLAM) is considered to be an essential capability for intelligent vehicles and mobile robots. However, most of the current lidar SLAM approaches are based on the assumption of a static environment. Hence the localization in a dynamic environment with multiple m...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
19.06.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Simultaneous Localization and Mapping (SLAM) is considered to be an essential
capability for intelligent vehicles and mobile robots. However, most of the
current lidar SLAM approaches are based on the assumption of a static
environment. Hence the localization in a dynamic environment with multiple
moving objects is actually unreliable. The paper proposes a dynamic SLAM
framework RF-LIO, building on LIO-SAM, which adds adaptive multi-resolution
range images and uses tightly-coupled lidar inertial odometry to first remove
moving objects, and then match lidar scan to the submap. Thus, it can obtain
accurate poses even in high dynamic environments. The proposed RF-LIO is
evaluated on both self-collected datasets and open Urbanloco datasets. The
experimental results in high dynamic environments demonstrate that, compared
with LOAM and LIO-SAM, the absolute trajectory accuracy of the proposed RF-LIO
can be improved by 90% and 70%, respectively. RF-LIO is one of the
state-of-the-art SLAM systems in high dynamic environments. |
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DOI: | 10.48550/arxiv.2206.09463 |