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On one hand, the robot performs simultaneous localization and mapping (SLAM) based on the constrained local submap filter (CLSF).
Karto SLAM based on LIDAR also showed a large position error of 6.44 m.
SLAM based on LIDAR uses scan matching to localize the robot positions.
Open image in new window Fig. 4 Hubs in SLAM (based on [16, 43]).
Actually, such dependencies are inevitable, especially when dealing with a SLAM based on a probabilistic approach.
However, c Karto SLAM based on LIDAR map has no consistency because large distortions are apparent on the map.
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The proposed approach employs two SLAMs based on GPS and LIDAR and the RBPF weight of importance for sensor fusion.
Linear SLAM was recently demonstrated based on submap joining techniques in which a nonlinear coordinate transformation was performed separately out of the optimization loop, resulting in a convex optimization problem.
SLAM was based on Rao-Blackwellized particle filters [21].
SLAM implementations based on extended Kalman filter (EKF) data fusion have traditionally relied on simple geometric models for defining landmarks.
A 6-degree-of-freedom monocular SLAM method based on the delayed inverse-depth (DI-D) feature initialization is used as a benchmark.
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