13 Result(s)
within
Rudolph Triebel
Engineering
-
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...
-
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),...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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 ...
