Mapping with Dynamic-Object Probabilities Calculated from Single 3D Range Scans

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.