This paper introduces a robust normal vector estimator for point cloud data. It can handle sharp features as well as smooth areas. Our method is based on the inclusion of a robust estimator into a Principal Component Analysis in the neighborhood of the studied point which can detect and reject outliers automatically during the estimation. A projection process ensures robustness against noise. Two automatic initializations are computed leading to independent optimizations making the algorithm robust to neighborhood anisotropy around sharp features. An evaluation has been carried out in which the algorithm is compared to state-of-the-art methods. The results show that it is more robust against a low and/or a non-uniform sampling, a high noise level and outliers. Moreover, our algorithm is fast relatively to existing methods handling sharp features. The code and data sets will be available online.

Acquiring 3D data with LiDAR systems involves scanning multiple scenes from different points of view. In actual systems, the ICP algorithm (Iterative Closest Point) is commonly used to register the acquired point clouds together to form a unique one. However, this method faces local minima issues and often needs a coarse initial alignment to converge to the optimum. This paper develops a new method for registration adapted to indoor environments and based on structure priors of such scenes. Our method works without odometric data or physical targets. The rotation and translation of the rigid transformation are computed separately, using, respectively, the Gaussian image of the point clouds and a correlation of histograms. To evaluate our algorithm on challenging registration cases, two datasets were acquired and are available for comparison with other methods online. The evaluation of our algorithm on four datasets against six existing methods shows that the proposed method is more robust against sampling and scene complexity. Moreover, the time performances enable a real-time implementation.Data Set: https://liris.cnrs.fr/3d-registration/ Data Set License: ODC Attribute License