73 citations · 222 across the 7 of their papers we have counts for
7 papers
DCT Maps: Compact Differentiable Lidar Maps Based on the Cosine Transform
Alexander Schaefer, Lukas Luft, Wolfram Burgard
Most robot mapping techniques for lidar sensors tessellate the environment into pixels or voxels and assume uniformity of the environment within them. Although intuitive, this repr…
A Maximum Likelihood Approach to Extract Polylines from 2-D Laser Range Scans
Alexander Schaefer, Daniel Büscher, Lukas Luft +1
Man-made environments such as households, offices, or factory floors are typically composed of linear structures. Accordingly, polylines are a natural way to accurately represent t…
Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans
Alexander Schaefer, Daniel Büscher, Johan Vertens +2
Due to their ubiquity and long-term stability, pole-like objects are well suited to serve as landmarks for vehicle localization in urban environments. In this work, we present a co…
Closed-Form Full Map Posteriors for Robot Localization with Lidar Sensors
Lukas Luft, Alexander Schaefer, Tobias Schubert +1
A popular class of lidar-based grid mapping algorithms computes for each map cell the probability that it reflects an incident laser beam. These algorithms typically determine the…
An Analytical Lidar Sensor Model Based on Ray Path Information
Alexander Schaefer, Lukas Luft, Wolfram Burgard
Two core competencies of a mobile robot are to build a map of the environment and to estimate its own pose on the basis of this map and incoming sensor readings. To account for the…
Inferring latent structures via information inequalities
R. Chaves, L. Luft, T. O. Maciel +3
One of the goals of probabilistic inference is to decide whether an empirically observed distribution is compatible with a candidate Bayesian network. However, Bayesian networks wi…
Causal structures from entropic information: Geometry and novel scenarios
Rafael Chaves, Lukas Luft, David Gross
The fields of quantum non-locality in physics, and causal discovery in machine learning, both face the problem of deciding whether observed data is compatible with a presumed causa…