| 1. |
- Thippur, Akshaya, et al.
(författare)
-
KTH-3D-TOTAL : A 3D dataset for discovering spatial structures for long-term autonomous learning
- 2014
-
Ingår i: 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014. - : IEEE. - 9781479951994 ; , s. 1528-1535
-
Konferensbidrag (refereegranskat)abstract
- Long-term autonomous learning of human environments entails modelling and generalizing over distinct variations in: object instances in different scenes, and different scenes with respect to space and time. It is crucial for the robot to recognize the structure and context in spatial arrangements and exploit these to learn models which capture the essence of these distinct variations. Table-tops posses a typical structure repeatedly seen in human environments and are identified by characteristics of being personal spaces of diverse functionalities and dynamically changing due to human interactions. In this paper, we present a 3D dataset of 20 office table-tops manually observed and scanned 3 times a day as regularly as possible over 19 days (461 scenes) and subsequently, manually annotated with 18 different object classes, including multiple instances. We analyse the dataset to discover spatial structures and patterns in their variations. The dataset can, for example, be used to study the spatial relations between objects and long-term environment models for applications such as activity recognition, context and functionality estimation and anomaly detection.
|
|
| 2. |
- Caputo, B., et al.
(författare)
-
Overview of the CLEF 2009 robot vision track
- 2009
-
Ingår i: CLEF2009 Working Notes. - : CEUR-WS.
-
Konferensbidrag (refereegranskat)abstract
- The robot vision task has been proposed to the ImageCLEF participants for the first time in 2009. The task attracted a considerable attention, with 19 inscribed research groups, 7 groups eventually participating and a total of 27 submitted runs. The task addressed the problem of visual place recognition applied to robot topological localization. Specifically, participants were asked to classify rooms on the basis of image sequences, captured by a perspective camera mounted on a mobile robot. The sequences were acquired in an office environment, under varying illumination conditions and across a time span of almost two years. The training and validation set consisted of a subset of the IDOL2 database1. The test set consisted of sequences similar to those in the training and validation set, but acquired 20 months later and imaging also additional rooms. Participants were asked to build a system able to answer the question "where are you?" (I am in the kitchen, in the corridor, etc) when presented with a test sequence imaging rooms seen during training, or additional rooms that were not imaged in the training sequence. The system had to assign each test image to one of the rooms present in the training sequence, or indicate that the image came from a new room. We asked all participants to solve the problem separately for each test image (obligatory task). Additionally, results could also be reported for algorithms exploiting the temporal continuity of the image sequences (optional task). Of the 27 runs, 21 were submitted to the obligatory task, and 6 to the optional task. The best result in the obligatory task was obtained by the Multimedia Information Retrieval Group of the University of Glasgow, UK with an approach based on local feature matching. The best result in the optional task was obtained by the Intelligent Systems and Data Mining Group (SIMD) of the University of Castilla-La Mancha, Albacete, Spain, with an approach based on local features and a particle filter.
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
- Hawes, N., et al.
(författare)
-
Home alone : Autonomous extension and correction of spatial representations
- 2011
-
Konferensbidrag (refereegranskat)abstract
- In this paper we present an account of the problems faced by a mobile robot given an incomplete tour of an unknown environment, and introduce a collection of techniques which can generate successful behaviour even in the presence of such problems. Underlying our approach is the principle that an autonomous system must be motivated to act to gather new knowledge, and to validate and correct existing knowledge. This principle is embodied in Dora, a mobile robot which features the aforementioned techniques: shared representations, non-monotonic reasoning, and goal generation and management. To demonstrate how well this collection of techniques work in real-world situations we present a comprehensive analysis of the Dora system's performance over multiple tours in an indoor environment. In this analysis Dora successfully completed 18 of 21 attempted runs, with all but 3 of these successes requiring one or more of the integrated techniques to recover from problems.
|
|
| 6. |
- Luo, J., et al.
(författare)
-
Incremental learning for place recognition in dynamic environments
- 2007
-
Ingår i: Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on. - : IEEE. - 9781424409129 ; , s. 721-728
-
Konferensbidrag (refereegranskat)abstract
- Vision-based place recognition is a desirable feature for an autonomous mobile system. In order to work in realistic scenarios, visual recognition algorithms should be adaptive, i.e. should be able to learn from experience and adapt continuously to changes in the environment. This paper presents a discriminative incremental learning approach to place recognition. We use a recently introduced version of the incremental SVM, which allows to control the memory requirements as the system updates its internal representation. At the same time, it preserves the recognition performance of the batch algorithm. In order to assess the method, we acquired a database capturing the intrinsic variability of places over time. Extensive experiments show the power and the potential of the approach.
|
|
| 7. |
- Pronobis, Andrzej, et al.
(författare)
-
A realistic benchmark for visual indoor place recognition
- 2010
-
Ingår i: Robotics and Autonomous Systems. - : Elsevier BV. - 0921-8890 .- 1872-793X. ; 58:1, s. 81-96
-
Tidskriftsartikel (refereegranskat)abstract
- 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. Recent advances in vision have made this modality a viable alternative to the traditional range sensors, and visual place recognition algorithms emerged as a useful and widely applied tool for obtaining information about robot's position. Several place recognition methods have been proposed using vision alone or combined with sonar and/or laser. This research calls for standard benchmark datasets for development, evaluation and comparison of solutions. To this end, this paper presents two carefully designed and annotated image databases augmented with an experimental procedure and extensive baseline evaluation. The databases were gathered in an uncontrolled indoor office environment using two mobile robots and a standard camera. The acquisition spanned across a time range of several months and different illumination and weather conditions. Thus, the databases are very well suited for evaluating the robustness of algorithms with respect to a broad range of variations, often occurring in real-world settings. We thoroughly assessed the databases with a purely appearance-based place recognition method based on support vector machines and two types of rich visual features (global and local).
|
|
| 8. |
- Pronobis, Andrzej, et al.
(författare)
-
Multi-modal Semantic Place Classification
- 2010
-
Ingår i: The international journal of robotics research. - : SAGE Publications. - 0278-3649 .- 1741-3176. ; 29:2-3, s. 298-320
-
Tidskriftsartikel (refereegranskat)abstract
- The ability to represent knowledge about space and its position therein is crucial for a mobile robot. To this end, topological and semantic descriptions are gaining popularity for augmenting purely metric space representations. In this paper we present a multi-modal place classification system that allows a mobile robot to identify places and recognize semantic categories in an indoor environment. The system effectively utilizes information from different robotic sensors by fusing multiple visual cues and laser range data. This is achieved using a high-level cue integration scheme based on a Support Vector Machine (SVM) that learns how to optimally combine and weight each cue. Our multi-modal place classification approach can be used to obtain a real-time semantic space labeling system which integrates information over time and space. We perform an extensive experimental evaluation of the method for two different platforms and environments, on a realistic off-line database and in a live experiment on an autonomous robot. The results clearly demonstrate the effectiveness of our cue integration scheme and its value for robust place classification under varying conditions.
|
|
| 9. |
- Ullah, Muhammad Muneeb, et al.
(författare)
-
Towards Robust Place Recognition for Robot Localization
- 2008
-
Ingår i: 2008 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-9. ; , s. 530-537
-
Konferensbidrag (refereegranskat)abstract
- 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.
|
|
