Large-Scale Volumetric Scene Reconstruction using LiDAR

Large-scale 3D scene reconstruction is an important task in autonomous driving and other robotics applications as having an accurate representation of the environment is necessary to safely interact with it. Reconstructions are used for numerous tasks ranging from localization and mapping to planning. In robotics, volumetric depth fusion is the method of choice for indoor applications since the emergence of commodity RGB-D cameras due to its robustness and high reconstruction quality. In this work we present an approach for volumetric depth fusion using LiDAR sensors as they are common on most autonomous cars. We present a framework for large-scale mapping of urban areas considering loop closures. Our method creates a meshed representation of an urban area from recordings over a distance of 3.7km with a high level of detail on consumer graphics hardware in several minutes. The whole process is fully automated and does not need any user interference. We quantitatively evaluate our results from a real world application. Also, we investigate the effects of the sensor model that we assume on reconstruction quality by using synthetic data.

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