HDA-LVIO: A High-Precision LiDAR-Visual-Inertial Odometry in Urban Environments with Hybrid Data Association
CoRR(2024)
摘要
To enhance localization accuracy in urban environments, an innovative
LiDAR-Visual-Inertial odometry, named HDA-LVIO, is proposed by employing hybrid
data association. The proposed HDA_LVIO system can be divided into two
subsystems: the LiDAR-Inertial subsystem (LIS) and the Visual-Inertial
subsystem (VIS). In the LIS, the LiDAR pointcloud is utilized to calculate the
Iterative Closest Point (ICP) error, serving as the measurement value of Error
State Iterated Kalman Filter (ESIKF) to construct the global map. In the VIS,
an incremental method is firstly employed to adaptively extract planes from the
global map. And the centroids of these planes are projected onto the image to
obtain projection points. Then, feature points are extracted from the image and
tracked along with projection points using Lucas-Kanade (LK) optical flow.
Next, leveraging the vehicle states from previous intervals, sliding window
optimization is performed to estimate the depth of feature points.
Concurrently, a method based on epipolar geometric constraints is proposed to
address tracking failures for feature points, which can improve the accuracy of
depth estimation for feature points by ensuring sufficient parallax within the
sliding window. Subsequently, the feature points and projection points are
hybridly associated to construct reprojection error, serving as the measurement
value of ESIKF to estimate vehicle states. Finally, the localization accuracy
of the proposed HDA-LVIO is validated using public datasets and data from our
equipment. The results demonstrate that the proposed algorithm achieves
obviously improvement in localization accuracy compared to various existing
algorithms.
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