Tightly Coupled 3D Lidar Inertial Odometry and Mapping

Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations. We introduce a tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing the...

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Bibliographic Details
Published inProceedings - IEEE International Conference on Robotics and Automation pp. 3144 - 3150
Main Authors Ye, Haoyang, Chen, Yuying, Liu, Ming
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2019
Subjects
Estimation
Feature extraction
Laser radar
Optimization
Robot sensing systems
Three-dimensional displays
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Summary:Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations. We introduce a tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing the cost derived from lidar and IMU measurements, the lidarIMU odometry (LIO) can perform well with considerable drifts after long-term experiment, even in challenging cases where the lidar measurement can be degraded. Besides, to obtain more reliable estimations of the lidar poses, a rotation-constrained refinement algorithm (LIO-mapping) is proposed to further align the lidar poses with the global map. The experiment results demonstrate that the proposed method can estimate the poses of the sensor pair at the IMU update rate with high precision, even under fast motion conditions or with insufficient features.
ISSN:2577-087X
DOI:10.1109/ICRA.2019.8793511