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Chapter and Conference Paper
Active Monte Carlo Localization in Outdoor Terrains Using Multi-level Surface Maps
In this paper we consider the problem of active mobile robot localization with range sensors in outdoor environments. In contrast to passive approaches our approach actively selects the orientation of the lase...
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Chapter
Non-Iterative Vision-Based Interpolation of 3D Laser Scans
3D range sensors, particularly 3D laser range scanners, enjoy a rising popularity and are used nowadays for many different applications. The resolution 3D range sensors provide in the image plane is typically ...
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Chapter and Conference Paper
Collective Classification for Labeling of Places and Objects in 2D and 3D Range Data
In this paper, we present an algorithm to identify types of places and objects from 2D and 3D laser range data obtained in indoor environments. Our approach is a combination of a collective classification meth...
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Chapter
Recovering the Shape of Objects in 3D Point Clouds with Partial Occlusions
In this paper we present an approach to label data points in 3d range scans and to use these labels to learn prototypical representations of objects. Our approach uses associative Markov networks (AMNs) to cal...
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Chapter
Monte Carlo Localization in Outdoor Terrains Using Multi-Level Surface Maps
In this paper we consider the problem of mobile robot localization with range sensors in outdoor environments. Our approach applies a particle filter to estimate the full six-dimensional state of the robot. To...
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Chapter and Conference Paper
Multiclass Multimodal Detection and Tracking in Urban Environments ⋆
This paper presents a novel approach to detect and track pedestrians and cars based on the combined information retrieved from a camera and a laser range scanner. Laser data points are classified using boosted...
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Chapter and Conference Paper
Exploiting Repetitive Object Patterns for Model Compression and Completion
Many man-made and natural structures consist of similar elements arranged in regular patterns. In this paper we present an unsupervised approach for discovering and reasoning on repetitive patterns of objects ...
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Chapter
A Bayesian Approach to Learning 3D Representations of Dynamic Environments
We propose a novel probabilistic approach to learning spatial representations of dynamic environments from 3D laser range measurements. Whilst most of the previous techniques developed in robotics address this...
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Chapter and Conference Paper
Environment-Adaptive Learning: How Clustering Helps to Obtain Good Training Data
In this paper, we propose a method to combine unsupervised and semi-supervised learning (SSL) into a system that is able to adaptively learn objects in a given environment with very little user interaction. Th...
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Chapter and Conference Paper
Active Online Learning for Interactive Segmentation Using Sparse Gaussian Processes
We present an active learning framework for image segmentation with user interaction. Our system uses a sparse Gaussian Process classifier (GPC) trained on manually labeled image pixels (user scribbles) and re...
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Chapter
Driven Learning for Driving: How Introspection Improves Semantic Map**
This paper explores the suitability of commonly employed classification methods to action-selection tasks in robotics, and argues that a classifier’s introspective capacity is a vital but as yet largely under-app...
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Chapter and Conference Paper
Non-rigid 3D Shape Retrieval via Large Margin Nearest Neighbor Embedding
In this paper, we propose a highly efficient metric learning approach to non-rigid 3D shape analysis. From a training set of 3D shapes from different classes, we learn a transformation of the shapes which opti...
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Chapter
SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports
We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in larg...
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Chapter
Unsupervised 3D Object Discovery and Categorization for Mobile Robots
We present a method for mobile robots to learn the concept of objects and categorize them without supervision using 3D point clouds from a laser scanner as input. In particular, we address the challenges of ca...
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Chapter and Conference Paper
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views ...
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Chapter and Conference Paper
6DoF Pose Estimation for Industrial Manipulation Based on Synthetic Data
We present a perception system for mobile manipulation tasks. The primary design goal of the proposed system is to minimize human interaction during system setup which is achieved by several means, such as au...
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Chapter and Conference Paper
Simultaneous Calibration and Map**
We present evaluation experiments of a hand-eye calibration and camera-camera calibration method, which is applicable to cases where classical calibration methods fail. As described in our earlier works, the c...
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Chapter and Conference Paper
3D Scene Reconstruction from a Single Viewport
We present a novel approach to infer volumetric reconstructions from a single viewport, based only on an RGB image and a reconstructed normal image. To overcome the problem of reconstructing regions in 3D that...
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Article
Augmented Autoencoders: Implicit 3D Orientation Learning for 6D Object Detection
We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views ...
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Chapter and Conference Paper
Robust Vision-Based Pose Correction for a Robotic Manipulator Using Active Markers
Robots with elastic or lightweight components are becoming common in research, but can suffer from undesired positioning imprecision, which motivates a vision-based pose correction of the manipulator. For robo...
