Registration of non-uniform density 3D laser scans for mapping with micro aerial vehicles

• We present a mapping system for non-uniform density 3D point clouds acquired by MAVs.
• To compensate for the different densities, we approximate the underlying surface.
• The extracted surface information is used to efficiently align the point clouds.
• We use the same surface-based error metric in a multi-edge pose graph optimization.
• We demonstrate 3D mapping with MAVs and superior performance to a single-edge system.

Micro aerial vehicles (MAVs) pose specific constraints on onboard sensing, mainly limited payload and limited processing power. For accurate 3D mapping even in GPS-denied environments, we have designed a lightweight 3D laser scanner specifically for the application on MAVs. Similar to other custom-built 3D laser scanners composed of a rotating 2D laser range finder, it exhibits different point densities within and between individual scan lines. When rotated fast, such non-uniform point densities influence neighborhood searches which in turn may negatively affect local feature estimation and scan registration. We present a complete pipeline for 3D mapping including pair-wise registration and global alignment of such non-uniform density 3D point clouds acquired in-flight. For registration, we extend a state-of-the-art registration algorithm to include topological information from approximate surface reconstructions. For global alignment, we use a graph-based approach making use of the same error metric and iteratively refine the complete vehicle trajectory. In experiments, we show that our approach can compensate for the effects caused by different point densities up to very low angular resolutions and that we can build accurate and consistent 3D maps in-flight with a micro aerial vehicle.