Organizational Unit:

Institute for Robotics and Intelligent Machines (IRIM)

Permanent Link

https://hdl.handle.net/1853/70657

Includes Organization(s)

Organizational Unit

Biorobotics and Human Modeling Lab

Organizational Unit

Humanoid Robotics Laboratory

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Publication Search Results

Now showing 1 - 10 of 10

Towards robust place recognition for robot localization

(Georgia Institute of Technology, 2008-05) Ullah, M. M. ; Pronobis, A. ; Caputo, B. ; Luo, J. ; Jensfelt, Patric ; Christensen, Henrik I.

Localization and context interpretation are two key competences for mobile robot systems. Visual place recognition, as opposed to purely geometrical models, holds promise of higher flexibility and association of semantics to the model. Ideally, a place recognition algorithm should be robust to dynamic changes and it should perform consistently when recognizing a room (for instance a corridor) in different geographical locations. Also, it should be able to categorize places, a crucial capability for transfer of knowledge and continuous learning. In order to test the suitability of visual recognition algorithms for these tasks, this paper presents a new database, acquired in three different labs across Europe. It contains image sequences of several rooms under dynamic changes, acquired at the same time with a perspective and omnidirectional camera, mounted on a socket. We assess this new database with an appearance-based algorithm that combines local features with support vector machines through an ad-hoc kernel. Results show the effectiveness of the approach and the value of the database.
The M-Space Feature Representation for SLAM

(Georgia Institute of Technology, 2007-10) Folkesson, John ; Jensfelt, Patric ; Christensen, Henrik I.

In this paper, a new feature representation for simultaneous localization and mapping (SLAM) is discussed. The representation addresses feature symmetries and constraints explicitly to make the basic model numerically robust. In previous SLAM work, complete initialization of features is typically performed prior to introduction of a new feature into the map. This results in delayed use of new data. To allow early use of sensory data, the new feature representation addresses the use of features that initially have been partially observed. This is achieved by explicitly modelling the subspace of a feature that has been observed. In addition to accounting for the special properties of each feature type, the commonalities can be exploited in the new representation to create a feature framework that allows for interchanging of SLAM algorithms, sensor and features. Experimental results are presented using a low-cost web-cam, a laser range scanner, and combinations thereof.
SLAM using Visual Scan-Matching with Distinguishable 3D Points

(Georgia Institute of Technology, 2006-10) Bertolli, Federico ; Jensfelt, Patric ; Christensen, Henrik I.

Scan-matching based on data from a laser scanner is frequently used for mapping and localization. This paper presents an scan-matching approach based instead on visual information from a stereo system. The Scale Invariant Feature Transform (SIFT) is used together with epipolar constraints to get high matching precision between the stereo images. Calculating the 3D position of the corresponding points in the world results in a visual scan where each point has a descriptor attached to it. These descriptors can be used when matching scans acquired from different positions. Just like in the work with laser based scan matching a map can be defined as a set of reference scans and their corresponding acquisition point. In essence this reduces each visual scan that can consist of hundreds of points to a single entity for which only the corresponding robot pose has to be estimated in the map. This reduces the overall complexity of the map. The SIFT descriptor attached to each of the points in the reference allows for robust matching and detection of loop closing situations. The paper presents real-world experimental results from an indoor office environment.
Attentional Landmark Selection for Visual SLAM

(Georgia Institute of Technology, 2006-10) Frintrop, Simone ; Jensfelt, Patric ; Christensen, Henrik I.

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.
A Discriminative Approach to Robust Visual Place Recognition

(Georgia Institute of Technology, 2006-10) Pronobis, A. ; Caputo, B. ; Jensfelt, Patric ; Christensen, Henrik I.

An important competence for a mobile robot system is the ability to localize and perform context interpretation. This is required to perform basic navigation and to facilitate local specific services. Usually localization is performed based on a purely geometric model. Through use of vision and place recognition a number of opportunities open up in terms of flexibility and association of semantics to the model. To achieve this the present paper presents an appearance based method for place recognition. The method is based on a large margin classifier in combination with a rich global image descriptor. The method is robust to variations in illumination and minor scene changes. The method is evaluated across several different cameras, changes in time-of-day and weather conditions. The results clearly demonstrate the value of the approach.
Design of an Office-Guide Robot for Social Interaction Studies

(Georgia Institute of Technology, 2006-10) Pacchierotti, Elena ; Christensen, Henrik I. ; Jensfelt, Patric

In this paper, the design of an office-guide robot for social interaction studies is presented. We are interested in studying the impact of passage behaviours in casual encounters. While the system offers assistance in locating the appropriate office that a visitor wants to reach, it is expected to engage in a passing behaviour to allow free passage for other persons that it may encounter. Through use of such an approach it is possible to study the effect of social interaction in a situation that is much more natural than out-of-context user studies. The system has been tested in an early evaluation phase when it worked for almost 7 hours. A total of 64 interactions with people were registered and 13 passage behaviors were performed to conclude that this framework can be successfully used for the evaluation of passing behaviors in natural contexts of operation.
Evaluation of Passing Distance for Social Robots

(Georgia Institute of Technology, 2006-09) Pacchierotti, Elena ; Christensen, Henrik I. ; Jensfelt, Patric

Casual encounters with mobile robots for nonexperts can be a challenge due to lack of an interaction model. The present work is based on the rules from proxemics which are used to design a passing strategy. In narrow corridors the lateral distance of passage is a key parameter to consider. An implemented system has been used in a small study to verify the basic parametric design for such a system. In total 10 subjects evaluated variations in proxemics for encounters with a robot in a corridor setting. The user feedback indicates that entering the intimate sphere of people is less comfortable, however a too significant avoidance is also considered unnecessary. Adequate signaling of avoidance is a behaviour that must be carefully tuned.
Pay Attention When Selecting Features

(Georgia Institute of Technology, 2006-08) Frintrop, Simone ; Jensfelt, Patric ; Christensen, Henrik I.

In this paper, we propose a new, hierarchical approach to landmark selection for simultaneous robot localization and mapping based on visual sensors: a biologically motivated attention system finds salient regions of interest (ROIs) in images, and within these regions, Harris corners are detected. This combines the advantages of the ROIs (reducing complexity, enabling good redetactability of regions) with the advantages of the Harris corners (high stability). Reducing complexity is important to meet real-time requirements and stability of features is essential to compute the depth of landmarks from structure from motion with a small baseline. We show that the number of landmarks is highly reduced compared to all Harris corners while maintaining the stability of features for the mapping task.
Clarification dialogues in human-augmented mapping

(Georgia Institute of Technology, 2006-03) Kruijff, Geert-Jan M. ; Zender, Hendrik ; Jensfelt, Patric ; Christensen, Henrik I.

An approach to dialogue based interaction for resolution of ambiguities encountered as part of Human-Augmented Mapping (HAM) is presented. The paper focuses on issues related to spatial organisation and localisation. The dialogue pattern naturally arises as robots are introduced to novel environments. The paper discusses an approach based on the notion of Questions under Discussion (QUD). The presented approach has been implemented on a mobile platform that has dialogue capabilities and methods for metric SLAM. Experimental results from a pilot study clearly demonstrate that the system can resolve problematic situations.
Exploiting Distinguishable Image Features in Robotic Mapping and Localization

(Georgia Institute of Technology, 2006-03) Jensfelt, Patric ; Folkesson, John ; Kragic, Danica ; Christensen, Henrik I.

Simultaneous localization and mapping (SLAM) is an important research area in robotics. Lately, systems that use a single bearing-only sensors have received significant attention and the use of visual sensors have been strongly advocated. In this paper, we present a framework for 3D bearing only SLAM using a single camera. We concentrate on image feature selection in order to achieve precise localization and thus good reconstruction in 3D. In addition, we demonstrate how these features can be managed to provide real-time performance and fast matching to detect loop-closing situations. The proposed vision system has been combined with an extended Kalman Filter (EKF) based SLAM method. A number of experiments have been performed in indoor environments which demonstrate the validity and effectiveness of the approach. We also show how the SLAM generated map can be used for robot localization. The use of vision features which are distinguishable allows a straightforward solution to the "kidnapped-robot" scenario.