8 Result(s)
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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
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
Applicability of Deep Learned vs Traditional Features for Depth Based Classification
In robotic applications, highly specific objects such as industrial parts, for example, often need to be recognized. In these cases methods can’t rely on the online availability of large labeled training data ...
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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
On the Use of the Tree Structure of Depth Levels for Comparing 3D Object Views
Today the simple availability of 3D sensory data, the evolution of 3D representations, and their application to object recognition and scene analysis tasks promise to improve autonomy and flexibility of robots...
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Chapter and Conference Paper
Object Categorization in Clutter Using Additive Features and Hashing of Part-Graph Descriptors
Detecting objects in clutter is an important capability for a household robot executing pick and place tasks in realistic settings. While approaches from 2D vision work reasonably well under certain lighting c...
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Chapter and Conference Paper
Reconstruction and Verification of 3D Object Models for Gras**
In this paper we present a method for approximating complete models of objects with 3D shape primitives, by exploiting common symmetries in objects of daily use. Our proposed approach reconstructs boxes and cy...
