Rudolph Triebel

TL;DR: This work proposes a real-time RGB-based pipeline for object detection and 6D pose estimation based on a variant of the Denoising Autoencoder trained on simulated views of a 3D model using Domain Randomization.

TL;DR: This paper proposes a new representation denoted as multi-level surface maps (MLS maps) which allows to store multiple surfaces in each cell of the grid and is well-suited for representing large-scale outdoor environments.

TL;DR: A new approach is presented that interleaves mapping and localization with a probabilistic technique to identify spurious measurements and generates accurate 2D and 3D in different kinds of dynamic indoor and outdoor environments.

TL;DR: To build consistent maps of large mines with many cycles, an algorithm for estimating global correspondences and aligning robot paths is described, which enables us to recover consistent maps several hundreds of meters in diameter, without odometric information.

TL;DR: How the SPENCER project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors is described.