Mark Pfeiffer

Fabio Ruetz, Emili Hernández, Mark Pfeiffer et al.

This paper presents a novel approach for local 3D environment representation for autonomous unmanned ground vehicle (UGV) navigation called On Visible Point Clouds Mesh(OVPC Mesh). Our approach represents the surrounding of the robot as a watertight 3D mesh generated from local point cloud data in order to represent the free space surrounding the robot. It is a conservative estimation of the free space and provides a desirable trade-off between representation precision and computational efficiency, without having to discretize the environment into a fixed grid size. Our experiments analyze the usability of the approach for UGV navigation in rough terrain, both in simulation and in a fully integrated real-world system. Additionally, we compare our approach to well-known state-of-the-art solutions, such as Octomap and Elevation Mapping and show that OVPC Mesh can provide reliable 3D information for trajectory planning while fulfilling real-time constraints.

Mark Pfeiffer, Giuseppe Paolo, Hannes Sommer et al.

This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians. Human navigation behavior is mostly influenced by their surrounding pedestrians and by the static obstacles in their vicinity. In this paper we introduce a new model based on Long-Short Term Memory (LSTM) neural networks, which is able to learn human motion behavior from demonstrated data. To the best of our knowledge, this is the first approach using LSTMs, that incorporates both static obstacles and surrounding pedestrians for trajectory forecasting. As part of the model, we introduce a new way of encoding surrounding pedestrians based on a 1d-grid in polar angle space. We evaluate the benefit of interaction-aware motion prediction and the added value of incorporating static obstacles on both simulation and real-world datasets by comparing with state-of-the-art approaches. The results show, that our new approach outperforms the other approaches while being very computationally efficient and that taking into account static obstacles for motion predictions significantly improves the prediction accuracy, especially in cluttered environments.