911 citations · 2.9k across the 99 of their papers we have counts for
19 papers · 1 filter
Self-Supervised Visual Terrain Classification from Unsupervised Acoustic Feature Learning
Jannik Zürn, Wolfram Burgard, Abhinav Valada
Mobile robots operating in unknown urban environments encounter a wide range of complex terrains to which they must adapt their planned trajectory for safe and efficient navigation…
Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution
Alberto Pretto, Stéphanie Aravecchia, Wolfram Burgard +25
The application of autonomous robots in agriculture is gaining increasing popularity thanks to the high impact it may have on food security, sustainability, resource use efficiency…
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 Finite Planes from 3-D Laser Scans
Alexander Schaefer, Johan Vertens, Daniel Büscher +1
Whether it is object detection, model reconstruction, laser odometry, or point cloud registration: Plane extraction is a vital component of many robotic systems. In this paper, we…
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…
Self-supervised 3D Shape and Viewpoint Estimation from Single Images for Robotics
Oier Mees, Maxim Tatarchenko, Thomas Brox +1
We present a convolutional neural network for joint 3D shape prediction and viewpoint estimation from a single input image. During training, our network gets the learning signal fr…
Adversarial Skill Networks: Unsupervised Robot Skill Learning from Video
Oier Mees, Markus Merklinger, Gabriel Kalweit +1
Key challenges for the deployment of reinforcement learning (RL) agents in the real world are the discovery, representation and reuse of skills in the absence of a reward function.…
Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control
Lukas Hermann, Max Argus, Andreas Eitel +3
We propose Adaptive Curriculum Generation from Demonstrations (ACGD) for reinforcement learning in the presence of sparse rewards. Rather than designing shaped reward functions, AC…
Learning User Preferences for Trajectories from Brain Signals
Henrich Kolkhorst, Wolfram Burgard, Michael Tangermann
Robot motions in the presence of humans should not only be feasible and safe, but also conform to human preferences. This, however, requires user feedback on the robot's behavior.…
Combined Task and Action Learning from Human Demonstrations for Mobile Manipulation Applications
Tim Welschehold, Nichola Abdo, Christian Dornhege +1
Learning from demonstrations is a promising paradigm for transferring knowledge to robots. However, learning mobile manipulation tasks directly from a human teacher is a complex pr…
Learning Multimodal Fixed-Point Weights using Gradient Descent
Lukas Enderich, Fabian Timm, Lars Rosenbaum +1
Due to their high computational complexity, deep neural networks are still limited to powerful processing units. To promote a reduced model complexity by dint of low-bit fixed-poin…
CMRNet: Camera to LiDAR-Map Registration
Daniele Cattaneo, Matteo Vaghi, Augusto Luis Ballardini +3
In this paper we present CMRNet, a realtime approach based on a Convolutional Neural Network to localize an RGB image of a scene in a map built from LiDAR data. Our network is not…
DeepTemporalSeg: Temporally Consistent Semantic Segmentation of 3D LiDAR Scans
Ayush Dewan, Wolfram Burgard
Understanding the semantic characteristics of the environment is a key enabler for autonomous robot operation. In this paper, we propose a deep convolutional neural network (DCNN)…
Vision-Based Autonomous UAV Navigation and Landing for Urban Search and Rescue
Mayank Mittal, Rohit Mohan, Wolfram Burgard +1
Unmanned Aerial Vehicles (UAVs) equipped with bioradars are a life-saving technology that can enable identification of survivors under collapsed buildings in the aftermath of natur…
Scheduled Intrinsic Drive: A Hierarchical Take on Intrinsically Motivated Exploration
Jingwei Zhang, Niklas Wetzel, Nicolai Dorka +2
Exploration in sparse reward reinforcement learning remains an open challenge. Many state-of-the-art methods use intrinsic motivation to complement the sparse extrinsic reward sign…
Robot Localization in Floor Plans Using a Room Layout Edge Extraction Network
Federico Boniardi, Abhinav Valada, Rohit Mohan +2
Indoor localization is one of the crucial enablers for deployment of service robots. Although several successful techniques for indoor localization have been proposed, the majority…