Armin B. Cremers

AAAI Conference 2019 Conference Paper

Triple Classification Using Regions and Fine-Grained Entity Typing

• Tiansi Dong
• Zhigang Wang
• Juanzi Li
• Christian Bauckhage
• Armin B. Cremers

A Triple in knowledge-graph takes a form that consists of head, relation, tail. Triple Classification is used to determine the truth value of an unknown Triple. This is a hard task for 1-to-N relations using the vector-based embedding approach. We propose a new region-based embedding approach using fine-grained type chains. A novel geometric process is presented to extend the vectors of pre-trained entities into n-balls (n-dimensional balls) under the condition that head balls shall contain their tail balls. Our algorithm achieves zero energy cost, therefore, serves as a case study of perfectly imposing tree structures into vector space. An unknown Triple (h, r, x) will be predicted as true, when x’s n-ball is located in the r-subspace of h’s n-ball, following the same construction of known tails of h. The experiments are based on large datasets derived from the benchmark datasets WN11, FB13, and WN18. Our results show that the performance of the new method is related to the length of the type chain and the quality of pre-trained entityembeddings, and that performances of long chains with welltrained entity-embeddings outperform other methods in the literature. Source codes and datasets are located at https: //github. com/GnodIsNait/mushroom.

ICRA Conference 2017 Conference Paper

Hierarchical salient object detection for assisted grasping

• Dominik Alexander Klein
• Boris Illing
• Bastian Gaspers
• Dirk Schulz 0001
• Armin B. Cremers

Visual scene decomposition into semantic entities is one of the major challenges when creating a reliable object grasping system. Recently, we introduced a bottom-up hierarchical clustering approach [1] which is able to segment objects and parts in a scene. In this paper, we introduce a transform from such a segmentation into a corresponding, hierarchical saliency function. In comprehensive experiments we demonstrate its ability to detect salient objects in a scene. Furthermore, this hierarchical saliency defines a most salient corresponding region (scale) for every point in an image. Based on this, an easy-to-use pick and place manipulation system was developed and tested exemplarily.

ICRA Conference 2015 Conference Paper

Efficient radius neighbor search in three-dimensional point clouds

• Jens Behley
• Volker Steinhage
• Armin B. Cremers

Finding all neighbors of a point inside a given radius is an integral part in many approaches using three-dimensional laser range data. We present novel insights to significantly improve the runtime performance of radius neighbor search using octrees. Our contributions are as follows: (1) We propose an index-based organization of the point cloud such that we can efficiently store start and end indexes of points inside every octant and (2) exploiting this representation, we can use pruning of irrelevant subtrees in the traversal to facilitate highly efficient radius neighbor search. We show significant runtime improvements of our proposed octree representation over state-of-the-art neighbor search implementations on three different urban datasets.

IROS Conference 2013 Conference Paper

Laser-based segment classification using a mixture of bag-of-words

• Jens Behley
• Volker Steinhage
• Armin B. Cremers

In this paper, we propose a segment-based object detection approach using laser range data. Our detection approach is built up of three stages: First, a hierarchical segmentation approach generates a hierarchy of coarse-to-fine segments to reduce the impact of over- and under-segmentation in later stages. Next, we employ a learned mixture model to classify all segments. The model combines multiple softmax regression classifiers learned on specific bag-of-word representations using different parameterizations of a descriptor. In the final stage, we filter irrelevant and duplicate detections using a greedy method in consideration of the segment hierarchy. We experimentally evaluate our approach on recently published real-world datasets to detect pedestrians, cars, and cyclists.

ICRA Conference 2012 Conference Paper

Performance of histogram descriptors for the classification of 3D laser range data in urban environments

• Jens Behley
• Volker Steinhage
• Armin B. Cremers

The selection of suitable features and their parameters for the classification of three-dimensional laser range data is a crucial issue for high-quality results. In this paper we compare the performance of different histogram descriptors and their parameters on three urban datasets recorded with various sensors-sweeping SICK lasers, tilting SICK lasers and a Velodyne 3D laser range scanner. These descriptors are 1D, 2D, and 3D histograms capturing the distribution of normals or points around a query point. We also propose a novel histogram descriptor, which relies on the spectral values in different scales. We argue that choosing a larger support radius and a z-axis based global reference frame/axis can boost the performance of all kinds of investigated classification models significantly. The 3D histograms relying on the point distribution, normal orientations, or spectral values, turned out to be the best choice for the classification in urban environments.

ICRA Conference 2011 Conference Paper

Boosting scalable gradient features for adaptive real-time tracking

• Dominik Alexander Klein
• Armin B. Cremers

Recently, several image gradient and edge based features have been introduced. In unison, they all discovered that object shape is a strong cue for recognition and tracking. Generally their basic feature extraction relies on pixel-wise gradient or edge computation using discrete filter masks, while scale invariance is later achieved by higher level operations like accumulating histograms or abstracting edgels to line segments. In this paper we show a novel and fast way to compute region based gradient features which are scale invariant themselves. We developed specialized, quick learnable weak classifiers that are integrated into our adaptively boosted observation model for particle filter based tracking. With an ensemble of region based gradient features this observation model is able to reliably capture the shape of the tracked object. The observation model is adapted to new object and background appearances while tracking. Thus we developed advanced methods to decide when to update the model, or in other words, if the filter is on target or not. We evaluated our approach using the BoBoT 1 as well as the PROST 2 datasets.

ICRA Conference 2011 Conference Paper

Person tracking in three-dimensional laser range data with explicit occlusion adaption

• Florian Schöler
• Jens Behley
• Volker Steinhage
• Dirk Schulz 0001
• Armin B. Cremers

This paper presents an approach to exploit the richer information of sensor data provided by 3d laser rangefinders for the purpose of person tracking. Introduced is a method to adapt the observation model of a particle filter, to identify partial and full occlusions of a person, to determine the amount of occlusion behind an obstacle, and the occluding obstacle itself. This is done by tracing rays from positions near the person to the sensor and determining whether the ray hits an obstacle. The laser range data is represented using a voxel grid, which facilitates efficient retrieval and data reduction. As our experiments show, our proposed tracking approach is able to reliably keep track of a person in real-time, even when only partially visible, when moving in uneven terrain, or when the person passes closely another person of different size.

IROS Conference 2010 Conference Paper

Adaptive real-time video-tracking for arbitrary objects

• Dominik Alexander Klein
• Dirk Schulz 0001
• Simone Frintrop
• Armin B. Cremers

In this paper, we present a visual object tracker for mobile systems that is able to specialize to individual objects during tracking. The core of our method is a novel observation model and the way it is automatically adapted to a changing object and background appearance over time. The model is integrated into the well known Condensation algorithm (SIR filter) for statistical inference, and it consists of a boosted ensemble of simple threshold classifiers built upon center-surround Haar-like features, which the filter continuously updates based on the images perceived. We present optimizations and reasonable approximations to limit the computational costs. Thus, the final algorithms are capable of processing video input at real-time. To experimentally investigate the gain of adapting the observation model we compare two different approaches with a non-adapting version of our observation model: maintaining a single observation model for all particles, and maintaining individual observation models for each particle. In addition, experiments were conducted to compare system performances between the proposed algorithms and two other state of the art Condensation based tracking approaches.

IROS Conference 2010 Conference Paper

Learning to hash logistic regression for fast 3D scan point classification

• Jens Behley
• Kristian Kersting
• Dirk Schulz 0001
• Volker Steinhage
• Armin B. Cremers

Segmenting range data into semantic categories has become a more and more active field of research in robotics. In this paper, we advocate to view this task as a problem of fast, large-scale retrieval. Intuitively, given a dataset of millions of labeled scan points and their neighborhoods, we simply search for similar points in the datasets and use the labels of the retrieved ones to predict the labels of a novel point using some local prediction model such as majority vote or logistic regression. However, actually carrying this out requires highly efficient ways of (1) storing millions of scan points in memory and (2) quickly finding similar scan points to a target scan point. In this paper, we propose to address both issues by employing Weiss et al. 's recent spectral hashing. It represents each item in a database by a compact binary code that is constructed so that similar items will have similar binary code words. In turn, similar neighbors have codes within a small Hamming distance of the code for the query. Then, we learn a logistic regression model locally over all points with the same binary code word. Our experiments on real world 3D scans show that the resulting approach, called spectrally hashed logistic regression, can be ultra fast at prediction time and outperforms state-of-the art approaches such as logistic regression and nearest neighbor.

ICRA Conference 2010 Conference Paper

Optimization techniques for laser-based 3D particle filter SLAM

• Jochen Welle
• Dirk Schulz 0001
• Thomas Bachran
• Armin B. Cremers

In recent years multiple simultaneous localization and mapping (SLAM) algorithms have been proposed, which address the challenges of 3D environments in combination with six degress of freedom in the robot position. Commonly, solutions based on scan-matching algorithms are applied. In contrast to these approaches, we propose to use a particle filter transferring the concept of the 2D Rao-Blackwellized particle filter SLAM to 3D. As filter input, 3D laser range data and odometry readings are obtained while the robot is in motion. The ground plane is estimated based on previously built map parts, thereby approaching the problem that not all degrees of freedom are covered by the odometry. To gain control of the high memory requirements for the particles' 3D map representations, we introduce a memory efficient search structure and adapt a technique to efficiently organize and share maps between particles. We evaluate our approach based on experimental results obtained by

ICRA Conference 2010 Conference Paper

Visual landmark generation and redetection with a single feature per frame

• Simone Frintrop
• Armin B. Cremers

In this paper we show that visual landmark generation and redetection is possible with a single feature per frame. The approach is based on the assumption that highly discriminative regions are easily redetectable in subsequent frames as well as in frames visited from different viewpoints. We investigate which feature detectors fit for this purpose and under which conditions the discriminability applies. The approach is tested in a topological localization scenario in which the best feature is tracked over several frames to build landmarks. We show that we can represent a large environment with a few salient landmarks and that a large percentage of these landmarks is robustly redetectable from different viewpoints.

ICRA Conference 2001 Conference Paper

Tracking Multiple Moving Targets with a Mobile Robot using Particle Filters and Statistical Data Association

• Dirk Schulz 0001
• Wolfram Burgard
• Dieter Fox
• Armin B. Cremers

One of the goals in the field of mobile robotics is the development of mobile platforms which operate in populated environments and offer various services to humans. For many tasks it is highly desirable that a robot can determine the positions of the humans in its surrounding. In this paper we present a method for tracking multiple moving objects with a mobile robot. We introduce a sample-based variant of joint probabilistic data association filters to track features originating from individual objects and to solve the correspondence problem between the detected features and the filters. In contrast to standard methods, occlusions are handled explicitly during data association. The technique has been implemented and tested on a real robot. Experiments carried out in a typical office environment show that the method is able to track multiple persons even when the trajectories of two people are crossing each other.

AIJ Journal 1999 Journal Article

Experiences with an interactive museum tour-guide robot

• Wolfram Burgard
• Armin B. Cremers
• Dieter Fox
• Dirk Hähnel
• Gerhard Lakemeyer
• Dirk Schulz
• Walter Steiner
• Sebastian Thrun

This article describes the software architecture of an autonomous, interactive tour-guide robot. It presents a modular and distributed software architecture, which integrates localization, mapping, collision avoidance, planning, and various modules concerned with user interaction and Web-based telepresence. At its heart, the software approach relies on probabilistic computation, on-line learning, and any-time algorithms. It enables robots to operate safely, reliably, and at high speeds in highly dynamic environments, and does not require any modifications of the environment to aid the robot's operation. Special emphasis is placed on the design of interactive capabilities that appeal to people's intuition. The interface provides new means for human-robot interaction with crowds of people in public places, and it also provides people all around the world with the ability to establish a “virtual telepresence” using the Web. To illustrate our approach, results are reported obtained in mid-1997, when our robot “RHINO” was deployed for a period of six days in a densely populated museum. The empirical results demonstrate reliable operation in public environments. The robot successfully raised the museum's attendance by more than 50%. In addition, thousands of people all over the world controlled the robot through the Web. We conjecture that these innovations transcend to a much larger range of application domains for service robots.

ICRA Conference 1999 Conference Paper

MINERVA: A Second-Generation Museum Tour-Guide Robot

• Sebastian Thrun
• Maren Bennewitz
• Wolfram Burgard
• Armin B. Cremers
• Frank Dellaert
• Dieter Fox
• Dirk Hähnel
• Charles R. Rosenberg

This paper describes an interactive tour-guide robot, which was successfully exhibited in a Smithsonian museum. During its two weeks of operation, the robot interacted with thousands of people, traversing more than 44 km at speeds of up to 163 cm/sec. Our approach specifically addresses issues such as safe navigation in unmodified and dynamic environments, and short-term human-robot interaction. It uses learning pervasively at all levels of the software architecture.

IROS Conference 1999 Conference Paper

Robust visualization of navigation experiments with mobile robots over the Internet

• Dirk Schulz 0001
• Wolfram Burgard
• Armin B. Cremers

Visualization is an important precondition for successful teleoperation of instructable mobile robots. Data connections with varying and limited bandwidth such as the Internet, however prohibit the continuous transmission of video signals. In this paper we propose a predictive simulation technique which is designed to permit the reliable visualization of the robot's actions over the Internet. It differs from previous approaches in that it includes an odometry and sensor simulation. This simulation of the robot allows the integration of a complete robot control system to reliably predict complex actions of the robot even if large transmission gaps of several seconds occur. We describe an application of the predictive simulation technique to navigation experiments with mobile robots. We present different experiments carried out with a real robot illustrating that the predictive simulation technique provides accurate visualizations of the robot's actions even if transmission gaps of more than ten seconds occur.

ICRA Conference 1999 Conference Paper

Semi-Automatic Acquisition of Symbolically-Annotated 3D-Models of Office Environments

• Michael Beetz
• Markus Giesenschlag
• Roman Englert
• Eberhard Gülch
• Armin B. Cremers

Describes a semi-automatic method for acquiring SA3D maps, maps that contain hierarchically structured 3D models of static, task relevant objects in the environment. Map acquisition is implemented as a two step process. In the first step, the robot acquires an approximate model that represents regions that might contain objects and indicate possibly occluding objects. This approximate model is then used to compute appropriate locations from where camera images should be taken. Object models are reconstructed interactively through human operators who place wireframe model in the camera images captured by the robot. The method is implemented and validated on an autonomous mobile robot.

ICRA Conference 1998 Conference Paper

A Hybrid Collision Avoidance Method for Mobile Robots

• Dieter Fox
• Wolfram Burgard
• Sebastian Thrun
• Armin B. Cremers

Proposes a hybrid approach to the problem of collision avoidance for indoor mobile robots. The /spl mu/DWA (model-based dynamic window approach) integrates sensor data from various sensors with information extracted from a map of the environment, to generate collision-free motion. A novel integration rule ensures that with high likelihood, the robot avoids collisions with obstacles not detectable with its sensors, even if it is uncertain about its position. The approach was implemented and tested extensively as part of an installation, in which a mobile robot gave interactive tours to visitors of the "Deutsches Museum Bonn. " Here our approach was essential for the success of the entire mission, because a large number of ill-shaped obstacles prohibited the use of purely sensor-based methods for collision avoidance.

IROS Conference 1998 Conference Paper

Integrating global position estimation and position tracking for mobile robots: the dynamic Markov localization approach

• Wolfram Burgard
• Andrcas Derr
• Dieter Fox
• Armin B. Cremers

Localization is one of the fundamental problems of mobile robots. In order to efficiently perform useful tasks such as office delivery, mobile robots must know their position in their environment. Existing approaches can be distinguished according to the type of localization problem they are designed to solve. Tracking techniques aim at monitoring the robot's position. They assume that the position is initially known and cannot recover from situations in which they lost track of the robot's position. Global localization techniques on the other hand, are able to estimate the robot's position under complete uncertainty. We present the dynamic Markov localization technique as a uniform approach to position estimation, which is able (1) to globally estimate the position of the robot, (2) to efficiently track its position whenever the robot's certainty is high, and (3) to detect and recover from localization failures. The approach has been implemented and intensively tested in real-world environments. We present several experiments illustrating the strength of our method.

ECAI Conference 1998 Conference Paper

Transparent, Flexible, and Resource-adaptive Image Processing for Autonomous Service Robots

• Michael Beetz
• Tom Arbuckle
• Armin B. Cremers
• M. Mann

TCS Journal 1977 Journal Article

On the formal definition of dependencies between the control and information structure of a data space

• Armin B. Cremers
• Thomas N. Hibbard

A data space is a general mathematical model for data types with a dynamic component (procedures). It consists of a set of objects (states), a set of functions for describing the information aspects of these objects, together with a control, i. e. a function mapping objects into objects. A set of properties is given which specify the formal relationship among the constituent components of a data space. The results of this paper especially concern the dependency between information structuring and control structuring. The mathematical analysis of this relationship is enhanced by focussing on the structure of so-called dependency sets, i. e. sets of objects which reflect the underlying control structure. The paper develops techniques for constructing meaningful dependency sets.

MFCS Conference 1976 Conference Paper

On the Relationship between a Procedure and its Data

• Armin B. Cremers
• Thomas N. Hibbard

MFCS Conference 1973 Conference Paper

On Vector Languages

• Armin B. Cremers
• Otto Mayer