16 Result(s)
within
Conference Paper
Rudolph Triebel
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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 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 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 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 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 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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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...
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Chapter and Conference Paper
Effective Version Space Reduction for Convolutional Neural Networks
In active learning, sampling bias could pose a serious inconsistency problem and hinder the algorithm from finding the optimal hypothesis. However, many methods for neural networks are hypothesis space agnosti...
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Chapter and Conference Paper
A Sparse Gaussian Approach to Region-Based 6DoF Object Tracking
We propose a novel, highly efficient sparse approach to region-based 6DoF object tracking that requires only a monocular RGB camera and the 3D object model. The key contribution of our work is a probabilistic ...
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Chapter and Conference Paper
Introspective Robot Perception Using Smoothed Predictions from Bayesian Neural Networks
This work focuses on improving uncertainty estimation in the field of object classification from RGB images and demonstrates its benefits in two robotic applications. We employ a Bayesian Neural Network (BNN),...
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Chapter and Conference Paper
Out-of-Distribution Detection for Adaptive Computer Vision
It is well known that computer vision can be unreliable when faced with previously unseen imaging conditions. This paper proposes a method to adapt camera parameters according to a normalizing flow-based out-o...
