Achim J. Lilienthal

TL;DR: Scan registration is an essential sub-task when building maps based on range finder data from mobile robots, and the problem is to deduce how the robot has moved between consecutive scans, based on the data collected.

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.

TL;DR: This work proposes a novel algorithm that achieves accurate point cloud registration an order of a magnitude faster than the current state of the art through the use of a compact spatial representation: the Three-Dimensional Normal Distributions Transform (3D-NDT).

TL;DR: The iterative closest points algorithm (ICP) is compared to the normal distributions transform (NDT) and an improved version of NDT is presented with a substantially larger valley of convergence than previously published versions.

TL;DR: This work presents a novel pseudo-gradient-based plume tracking algorithm and a particle filter-based source declaration approach, and applies it on a gas-sensitive micro-drone to solve the gas source localization task with mobile robots.