A large number of current mobile robots use 3D sensors as part of their sensor setup. Common 3D sensors, i.e., laser scanners or RGB-D cameras, emit a signal (laser light or infrared light for instance), and its reflection is recorded in order to estimate depth to a surface. The resulting set of measurement points is commonly referred to as 'point clouds'. In the first part of this dissertation an inherent problem of sensors that emit some light signal is addressed, namely that these signals can be reflected and/or refracted by transparent of highly specular surfaces, causing erroneous or missing measurements. A novel heuristic approach is introduced how such objects may nevertheless be identified and their size and shape reconstructed by fusing information from several viewpoints of the scene. In contrast to other existing approaches no prior knowledge about the objects is required nor is the shape of the reconstructed objects restricted to a limited set of geometric primitives. The thesis proceeds to illustrate problems caused by sensor noise and registration errors and introduces mechanisms to address these problems. Finally a quantitative comparison between equivalent directly measured objects, the reconstructions and "ground truth" is provided. The second part of the thesis addresses the problem of automatically determining the quality of the registration for a pair of point clouds. Although a different topic, these two problems are closely related, if modeled in the fashion of this thesis. After illustrating why the output parameters of a popular registration algorithm (ICP) are not suitable to deduce registration quality, several heuristic measures are developed that provide better insight. Experiments performed on different datasets were performed to showcase the applicability of the proposed measures in different scenarios.