Classification of Outdoor 3D Lidar Data Based on Unsupervised Gaussian Mixture Models

Three-dimensional point clouds acquired with lidars are an important source of data for the classification of outdoor environments by autonomous terrestrial robots. We propose a two-layer classification model. The first layer consists of a Gaussian mixture model. This model is determined in a training step in an unsupervised manner and classified into a large set of classes. The second layer consists of a grouping of these classes. This grouping is determined by an expert during the training step and leads to a smaller set of classes that are interpretable in a considered target task. Because the first layer relies on unsupervised learning, manual labeling of data is not required. Supervision is necessary only for the second layer and in this case is assisted by the classes provided by the first layer. The evaluation is done for two data sets acquired with different lidars and possessing different characteristics. It is done quantitatively using one of the data sets and qualitatively using another. The system design follows a standard learning procedure with training, validation, and test steps. The operation follows a standard classification pipeline. The system is simple with no requirement of preprocessing or postprocessing stages.