Author name cluster

Simone Frintrop

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

17 papers

1 author row

ICRA Conference 2022 Conference Paper

HD Ground - A Database for Ground Texture Based Localization

Jan Fabian Schmid
Stephan F. Simon
Raaghav Radhakrishnan
Simone Frintrop
Rudolf Mester

We present the HD Ground Database, a comprehensive database for ground texture based localization. It contains sequences of a variety of textures, obtained using a downward facing camera. In contrast to existing databases of ground images, the HD Ground Database is larger, has a greater variety of textures, and has a higher image resolution with less motion blur. Also, our database enables the first systematic study of how natural changes of the ground that occur over time affect localization performance, and it allows to examine a teach-and-repeat navigation scenario. We use the HD Ground Database to evaluate four state-of-the-art localization approaches for global localization, localization with the approximate pose being known, and relative localization.

ICRA Conference 2021 Conference Paper

CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds

Ge Gao
Mikko Lauri
Xiaolin Hu 0001
Jianwei Zhang 0001
Simone Frintrop

It is often desired to train 6D pose estimation systems on synthetic data because manual annotation is expensive. However, due to the large domain gap between the synthetic and real images, synthesizing color images is expensive. In contrast, this domain gap is considerably smaller and easier to fill for depth information. In this work, we present a system that regresses 6D object pose from depth information represented by point clouds, and a lightweight data synthesis pipeline that creates synthetic point cloud segments for training. We use an augmented autoencoder (AAE) for learning a latent code that encodes 6D object pose information for pose regression. The data synthesis pipeline only requires texture-less 3D object models and desired viewpoints, and it is cheap in terms of both time and hardware storage. Our data synthesis process is up to three orders of magnitude faster than commonly applied approaches that render RGB image data. We show the effectiveness of our system on the LineMOD, LineMOD Occlusion, and YCB Video datasets. The implementation of our system is available at: https://github.com/GeeeG/CloudAAE.

ICRA Conference 2020 Conference Paper

6D Object Pose Regression via Supervised Learning on Point Clouds

Ge Gao
Mikko Lauri
Yulong Wang
Xiaolin Hu 0001
Jianwei Zhang 0001
Simone Frintrop

This paper addresses the task of estimating the 6 degrees of freedom pose of a known 3D object from depth information represented by a point cloud. Deep features learned by convolutional neural networks from color information have been the dominant features to be used for inferring object poses, while depth information receives much less attention. However, depth information contains rich geometric information of the object shape, which is important for inferring the object pose. We use depth information represented by point clouds as the input to both deep networks and geometry-based pose refinement and use separate networks for rotation and translation regression. We argue that the axis-angle representation is a suitable rotation representation for deep learning, and use a geodesic loss function for rotation regression. Ablation studies show that these design choices outperform alternatives such as the quaternion representation and L2 loss, or regressing translation and rotation with the same network. Our simple yet effective approach clearly outperforms state-of-the-art methods on the YCB-video dataset.

IROS Conference 2019 Conference Paper

Explore, Approach, and Terminate: Evaluating Subtasks in Active Visual Object Search Based on Deep Reinforcement Learning

Jan Fabian Schmid
Mikko Lauri
Simone Frintrop

Searching for objects and distinguishing task-relevant objects from others is a key requirement for service robots. We propose a reinforcement learning solution to the active visual object search problem. Our method successfully learns to explore the environment, to approach the target object, and to decide when to terminate the search as the target object has been found. We demonstrate the efficiency of our solution on a dataset of real-world images collected by a robot. Our approach outperforms state-space planning or other baseline search strategies, reaching a higher success rate in a shorter time. We also study individual subtasks of active visual object search. Although strong baselines exist for the subtasks, our RL solution outperforms them in the overall search task.

ICRA Conference 2017 Conference Paper

Multi-robot active information gathering with periodic communication

Mikko Lauri
Eero Heinänen
Simone Frintrop

A team of robots sharing a common goal can benefit from coordination of the activities of team members, helping the team to reach the goal more reliably or quickly. We address the problem of coordinating the actions of a team of robots with periodic communication capability executing an information gathering task. We cast the problem as a multi-agent optimal decision-making problem with an information theoretic objective function. We show that appropriate techniques for solving decentralized partially observable Markov decision processes (Dec-POMDPs) are applicable in such information gathering problems. We quantify the usefulness of coordinated information gathering through simulation studies, and demonstrate the feasibility of the method in a real-world target tracking domain.

IROS Conference 2017 Conference Paper

Saliency-guided adaptive seeding for supervoxel segmentation

Ge Gao
Mikko Lauri
Jianwei Zhang 0001
Simone Frintrop

We propose a new saliency-guided method for generating supervoxels in 3D space. Rather than using an evenly distributed spatial seeding procedure, our method uses visual saliency to guide the process of supervoxel generation. This results in densely distributed, small, and precise supervoxels in salient regions which often contain objects, and larger supervoxels in less salient regions that often correspond to background. Our approach largely improves the quality of the resulting supervoxel segmentation in terms of boundary recall and under-segmentation error on publicly available benchmarks.

ICRA Conference 2015 Conference Paper

Saliency-based object discovery on RGB-D data with a late-fusion approach

Germán Martín García
Ekaterina Potapova
Thomas Werner
Michael Zillich
Markus Vincze
Simone Frintrop

We present a novel method based on saliency and segmentation to generate generic object candidates from RGB-D data. Our method uses saliency as a cue to roughly estimate the location and extent of the objects present in the scene. Salient regions are used to glue together the segments obtained from over-segmenting the scene by either color or depth segmentation algorithms, or by a combination of both. We suggest a late-fusion approach that first extracts segments from color and depth independently before fusing them to exploit that the data is complementary. Furthermore, we investigate several mechanisms for ranking the object candidates. We evaluate on one publicly available dataset and on one challenging sequence with a high degree of clutter. The results show that we are able to retrieve most objects in real-world indoor scenes and clearly outperform other state-of-the art methods.

ICRA Conference 2015 Conference Paper

Sequence-level object candidates based on saliency for generic object recognition on mobile systems

Esther Horbert
Germán Martín García
Simone Frintrop
Bastian Leibe

In this paper, we propose a novel approach for generating generic object candidates for object discovery and recognition in continuous monocular video. Such candidates have recently become a popular alternative to exhaustive window-based search as basis for classification. Contrary to previous approaches, we address the candidate generation problem at the level of entire video sequences instead of at the single image level. We propose a processing pipeline that starts from individual region candidates and tracks them over time. This enables us to group candidates for similar objects and to automatically filter out inconsistent regions. For generating the per-frame candidates, we introduce a novel multi-scale saliency approach that achieves a higher per-frame recall with fewer candidates than current state-of-the-art methods. Taken together, those two components result in a significant reduction of the number of object candidates compared to frame level methods, while keeping a consistently high recall.

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.

ICRA Conference 2010 Conference Paper

General object tracking with a component-based target descriptor

Simone Frintrop

In this paper, we present a component-based visual object tracker for mobile platforms. The core of the technique is a component-based descriptor that captures the structure and appearance of a target in a flexible way. This descriptor can be learned quickly from a single training image and is easily adaptable to different objects. The descriptor is integrated into the observation model of a visual tracker based on the well known Condensation algorithm. We show that the approach is applicable to a large variety of objects and in different environments with cluttered backgrounds and a moving camera. The method is robust to illumination and viewpoint changes and applicable to indoor as well as outdoor scenes.

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 2009 Conference Paper

Most salient region tracking

Simone Frintrop
Markus Kessel

In this paper, we introduce a cognitive approach for object tracking from a mobile platform. The approach is based on a biologically motivated attention system which is able to detect regions of interest in images based on concepts of the human visual system. A top-down guided visual search module of the system enables to especially favor features which fit to a previously learned target object. Here, the appearance of an object is learned online within the first image in which it is detected. In subsequent images, the attention system searches for the target features and builds a top-down, target-related saliency map. This enables to focus on the most relevant features of especially this object in especially this scene without knowing anything about a particular object model or scene in advance. The system is able to operate in real-time and to cope with the requirements of real-world tasks such as illumination variations and other moving objects.

ICRA Conference 2008 Conference Paper

Active gaze control for attentional visual SLAM

Simone Frintrop
Patric Jensfelt

In this paper, we introduce an approach to active camera control for visual SLAM. Features, detected by a biologically motivated attention system, are tracked over several frames to determine stable landmarks. Matching of features to database entries enables global loop closing. The focus of this paper is the active camera control module, which supports the system with three behaviours: i) A tracking behaviour tracks promising landmarks and prevents them from leaving the field of view. ii) A redetection behaviour directs the camera actively to regions where landmarks are expected and thus supports loop closing. iii) Finally, an exploration behaviour investigates regions without landmarks and enables a more uniform distribution of landmarks. Several real-world experiments show that the active camera control outperforms the passive system considerably.

IROS Conference 2006 Conference Paper

Attentional Landmark Selection for Visual SLAM

Simone Frintrop
Patric Jensfelt
Henrik I. Christensen

In this paper, we introduce a new method to automatically detect useful landmarks for visual SLAM. A biologically motivated attention system detects regions of interest which "pop-out" automatically due to strong contrasts and the uniqueness of features. This property makes the regions easily redetectable and thus they are useful candidates for visual landmarks. Matching based on scene prediction and feature similarity allows not only short-term tracking of the regions, but also redetection in loop closing situations. The paper demonstrates how regions are determined and how they are matched reliably. Various experimental results on real-world data show that the landmarks are useful with respect to be tracked in consecutive frames and to enable closing loops

ICRA Conference 2005 Conference Paper

Robust Object Detection at Regions of Interest with an Application in Ball Recognition

Sara Mitri
Simone Frintrop
Kai Pervölz
Hartmut Surmann
Andreas Nüchter

In this paper, we present a new combination of a biologically inspired attention system (VOCUS – Visual Object detection with a CompUtational attention System) with a robust object detection method. As an application, we built a reliable system for ball recognition in the RoboCup context. Firstly, VOCUS finds regions of interest generating a hypothesis for possible locations of the ball. Secondly, a fast classifier verifies the hypothesis by detecting balls at regions of interest. The combination of both approaches makes the system highly robust and eliminates false detections. Furthermore, the system is quickly adaptable to balls in different scenarios: The complex classifier is universally applicable to balls in every context and the attention system improves the performance by learning scenario-specific features quickly from only a few training examples.

IROS Conference 2004 Conference Paper

Saliency-based object recognition in 3D data

Simone Frintrop
Andreas Nüchter
Hartmut Surmann
Joachim Hertzberg

This paper presents a robust and real-time capable recognition system for the fast detection and classification of objects in spatial 3D data. Depth and reflection data from a 3D laser scanner are rendered into images and fed into a saliency-based visual attention system that detects regions of potential interest. Only these regions are examined by a fast classifier. The time saving of classifying objects in salient regions rather than in complete images is linear with the number of trained object classes. Robustness is achieved by the fusion of the bi-modal scanner data; in contrast to camera images, this data is completely illumination independent. The recognition system is trained for two different object classes and evaluated on real indoor data.

ICRA Conference 2001 Conference Paper

Robust Localization Using Context in Omnidirectional Imaging

Lucas Paletta
Simone Frintrop
Joachim Hertzberg

This work presents the concept to recover and utilize the visual context in panoramic images. Omnidirectional imaging has become recently an efficient basis for robot navigation. The proposed Bayesian reasoning over local image appearances enables to reject false hypotheses which do not fit the structural constraints in corresponding feature trajectories. The methodology is proved with real image data from an office robot to dramatically increase the localization performance in the presence of severe occlusion effects, particularly in noisy environments, and to recover rotational information on the fly.