Various autonomous robotic systems require maps for robust and safe navigation. Particularly when robots are employed in dynamic environments, accurate knowledge about which components of the robot perceptions belong to dynamic and static aspects in the environment can greatly improve navigation functions. In this paper we propose a novel method for building 3D grid maps using laser range data in dynamic environments. Our approach uses a neural network to estimate the pointwise probability of a point belonging to a dynamic object. The output from our network is fed to the mapping module for building a 3D grid map containing only static parts of the environment. We present experimental results obtained by training our neural network using the KITTI dataset and evaluating it in a mapping process using our own dataset. In extensive experiments, we show that maps generated using the proposed probability about dynamic objects increases the accuracy of the resulting maps.

To determine the pose of a vehicle is a fundamental problem in mobile robotics. Most approaches relate the current sensor observations to a map generated with previously acquired data of the same system or by another system with a similar sensor setup. Unfortunately, previously acquired data is not always available. In outdoor settings, GPS is a very useful tool to determine a global estimate of the vehicles pose. Unfortunately, GPS tends to be unreliable in situations in which a clear view to the sky is restricted. Yet, one can make use of publicly available map material as prior information. In this paper, we describe an approach to localize a robot equipped with a 3D range scanner with respect to a road network created from OpenStreetMap data. To successfully localize a mobile robot we propose a road classification scheme for 3D range data together with a novel sensor model, which relates the classification results to a road network. Compared to other approaches, our system does not require the robot to actually travel on the road network. We evaluate our approach in extensive experiments on simulated and real data and compare favorably to two state-of-the-art methods on those data.

Being able to estimate the dirt distribution in an environment makes it possible to compute efficient paths for robotic cleaners. In this paper, we present a novel approach for modeling and estimating the dynamics of the generation of dirt in an environment. Our model uses cell-wise Poisson processes on a regular grid to estimate the distribution of dirt in the environment. It allows for an effective estimation of the dynamics of the generation of dirt and for making predictions about the absolute dirt values. We propose two efficient cleaning policies that are based on the estimated dirt distributions and can easily be adapted to different needs of potential users. Through extensive experiments carried out with a modified iRobot Roomba vacuum cleaning robot and in simulation we demonstrate the effectiveness of our approach.