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7 DoF pose-graph optimization is employed to obtain geometrically consistent map.
Basically, relocalization is done for recovering a camera pose and loop detection is done for obtaining geometrically consistent map.
In SLAM, a robot incrementally builds a consistent map of the environment while simultaneously determining its posture within this map.
Therefore, to build a geometrically consistent map, the global optimization is performed in the recent vSLAM algorithms.
An accurate and consistent map of the distribution and intensity of budworm defoliation (as represented by the red discolouration) at the stand and within stand level is desirable.
Our experiments demonstrate the functionality of estimating the exact poses and their covariances in all 6DoF, leading to a globally consistent map.
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The use of symptom-based mappings allows for rapid and consistent mapping, but curation issues can result, and this is discussed more fully below.
Morphologic indicators of these fragic properties are often weakly expressed, making consistent mapping of soils difficult.
Consistent maps are obtained by simply overlapping estimated segments drawn from estimated poses.
Consistent mapping between different resolutions is performed by the means of sequences of restriction and prolongation operators.
In our study, both Fast-SLAM successfully aligned positions with GPS geo-referenced data and produced locally consistent maps.
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