Abstract
Abstract. For autonomous systems, an accurate and precise map of the environment is of importance. Mapping from LiDAR point clouds is one of the promising ways to generate 3D environment models. However, there are many problems caused by inaccurate data, missing areas, low density of points and sensor noise. Also, it is often not possible or accurate enough to generate a map from only one measurement campaign. In this paper, we propose a method to incrementally refine the map by several measurements from different campaigns and represent the map in a hierarchical way with a measure indicating uncertainty and the level of detail for objects. The idea is thus to store all captured information with a tentative semantics and uncertainty – even when it is not yet complete. Hence, occulated areas are presented as well, which can be possibly improved by the supplemental observation from the next measurement campaign. The proposed 3D environment model framework and the incremental update method are evaluated using LiDAR scans obtained from Riegl Mobile Mapping System.
Highlights
A precise 3D map is of great importance to many applications, such like autonomous systems
Many researchers come up with methods to reconstruct the urban scene, during which many problems exist such as inaccurate data, missing areas, low density of points and sensor noise
We propose a method to refine the map by several measurements from different campaigns incrementally: the map is represented in a hierarchical way with a measure indicating uncertainty and the level of detail for objects
Summary
A precise 3D map is of great importance to many applications, such like autonomous systems. We propose a method to refine the map by several measurements from different campaigns incrementally: the map is represented in a hierarchical way with a measure indicating uncertainty and the level of detail for objects. This way, high-level objects like the façade structures are automatically modelled in an incremental and hierarchical way, as more data become available. The existing methods often just fill the unknown occluded region with their models, but none of them stores this information to indicate the uncertainty there This information is provided as a part of the integrity measure in our map and serves for the refinement. Each new measurement will contribute to the completion of the model and/or to an improvement of its accuracy and integrity
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