In this work, we introduce a novel method for two-dimensional occupancy mapping using Gaussian processes. We address mapping as the task of classifying the robot's environment between free and occupied regions. The biggest challenge when using Gaussian processes for this task is the size of the input datasets. We tackle this problem by introducing a novel kernel, able to use as input data aggregated into two-dimensional cells. Using this kernel, we achieve comparable performance to previous Gaussian process occupancy mapping techniques in a fraction of the time taken by them. The approach can also be used to convert popular occupancy grids into continuous Gaussian process occupancy maps.

Detecting changing traffic conditions is of primal importance for the safety of autonomous cars navigating in urban environments. Among the traffic situations that require more attention and careful planning, road junctions are the most significant. This work presents an empirical study of the application of well known machine learning techniques to create a robust method for road junction detection. Features are extracted from 3D pointclouds corresponding to single frames of data collected by a laser rangefinder. Three well known classifiers-support vector machines, adaptive boosting and artificial neural networks-are used to classify them into “junctions” or “roads”. The best performing classifier is used in the next stage, where structured classifiers-hidden Markov models and conditional random fields-are used to incorporate contextual information, in an attempt to improve the performance of the method. We tested and compared these approaches on datasets from two different 3D laser scanners, and in two different countries, Germany and Brazil.