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Publications (7 of 7) Show all publications
Selin, M., Tiger, M., Duberg, D., Heintz, F. & Jensfelt, P. (2019). Efficient Autonomous Exploration Planning of Large Scale 3D-Environments [Letter to the editor]. IEEE Robotics and Automation Letters
Open this publication in new window or tab >>Efficient Autonomous Exploration Planning of Large Scale 3D-Environments
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2019 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045Article in journal, Letter (Refereed) Epub ahead of print
Abstract [en]

Exploration is an important aspect of robotics, whether it is for mapping, rescue missions or path planning in an unknown environment. Frontier Exploration planning (FEP) and Receding Horizon Next-Best-View planning (RH-NBVP) are two different approaches with different strengths and weaknesses. FEP explores a large environment consisting of separate regions with ease, but is slow at reaching full exploration due to moving back and forth between regions. RH-NBVP shows great potential and efficiently explores individual regions, but has the disadvantage that it can get stuck in large environments not exploring all regions. In this work we present a method that combines both approaches, with FEP as a global exploration planner and RH-NBVP for local exploration. We also present techniques to estimate potential information gain faster, to cache previously estimated gains and to exploit these to efficiently estimate new queries.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Keywords
Search and Rescue Robots, Motion and Path Planning, Mapping
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-154335 (URN)10.1109/LRA.2019.2897343 (DOI)
Projects
FACT (SSF)WASP
Funder
Swedish Foundation for Strategic Research Wallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2019-02-05 Created: 2019-02-05 Last updated: 2019-03-06Bibliographically approved
Tiger, M. & Heintz, F. (2018). Gaussian Process Based Motion Pattern Recognition with Sequential Local Models. In: 2018 IEEE Intelligent Vehicles Symposium (IV): . Paper presented at Intelligent Vehicles Symposium 2018.
Open this publication in new window or tab >>Gaussian Process Based Motion Pattern Recognition with Sequential Local Models
2018 (English)In: 2018 IEEE Intelligent Vehicles Symposium (IV), 2018Conference paper, Published paper (Refereed)
Abstract [en]

Conventional trajectory-based vehicular traffic analysis approaches work well in simple environments such as a single crossing but they do not scale to more structurally complex environments such as networks of interconnected crossings (e.g. urban road networks). Local trajectory models are necessary to cope with the multi-modality of such structures, which in turn introduces new challenges. These larger and more complex environments increase the occurrences of non-consistent lack of motion and self-overlaps in observed trajectories which impose further challenges. In this paper we consider the problem of motion pattern recognition in the setting of sequential local motion pattern models. That is, classifying sub-trajectories from observed trajectories in accordance with which motion pattern that best explains it. We introduce a Gaussian process (GP) based modeling approach which outperforms the state-of-the-art GP based motion pattern approaches at this task. We investigate the impact of varying local model overlap and the length of the observed trajectory trace on the classification quality. We further show that introducing a pre-processing step filtering out stops from the training data significantly improves the classification performance. The approach is evaluated using real GPS position data from city buses driving in urban areas for extended periods of time.

Keywords
Motion Pattern Recognition, Situation Analysis and Planning, Traffic Flow and Management, Vision Sensing and Perception, Autonomous Driving
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-148724 (URN)
Conference
Intelligent Vehicles Symposium 2018
Projects
CUGSVRCADICSELLIITWASP
Funder
CUGS (National Graduate School in Computer Science)
Available from: 2018-06-18 Created: 2018-06-18 Last updated: 2018-12-04
Andersson, O., Ljungqvist, O., Tiger, M., Axehill, D. & Heintz, F. (2018). Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance. In: 2018 IEEE Conference on Decision and Control (CDC): . Paper presented at 2018 IEEE 57th Annual Conference on Decision and Control (CDC),17-19 December, Miami, Florida, USA (pp. 4467-4474). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance
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2018 (English)In: 2018 IEEE Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 4467-4474Conference paper, Published paper (Refereed)
Abstract [en]

A key requirement of autonomous vehicles is the capability to safely navigate in their environment. However, outside of controlled environments, safe navigation is a very difficult problem. In particular, the real-world often contains both complex 3D structure, and dynamic obstacles such as people or other vehicles. Dynamic obstacles are particularly challenging, as a principled solution requires planning trajectories with regard to both vehicle dynamics, and the motion of the obstacles. Additionally, the real-time requirements imposed by obstacle motion, coupled with real-world computational limitations, make classical optimality and completeness guarantees difficult to satisfy. We present a unified optimization-based motion planning and control solution, that can navigate in the presence of both static and dynamic obstacles. By combining optimal and receding-horizon control, with temporal multi-resolution lattices, we can precompute optimal motion primitives, and allow real-time planning of physically-feasible trajectories in complex environments with dynamic obstacles. We demonstrate the framework by solving difficult indoor 3D quadcopter navigation scenarios, where it is necessary to plan in time. Including waiting on, and taking detours around, the motions of other people and quadcopters.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Series
Conference on Decision and Control (CDC), ISSN 2576-2370 ; 2018
Keywords
Motion Planning, Optimal Control, Autonomous System, UAV, Dynamic Obstacle Avoidance, Robotics
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-152131 (URN)10.1109/CDC.2018.8618964 (DOI)9781538613955 (ISBN)9781538613948 (ISBN)9781538613962 (ISBN)
Conference
2018 IEEE 57th Annual Conference on Decision and Control (CDC),17-19 December, Miami, Florida, USA
Funder
VINNOVAKnut and Alice Wallenberg FoundationSwedish Foundation for Strategic Research ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsSwedish Research CouncilLinnaeus research environment CADICSCUGS (National Graduate School in Computer Science)
Note

This work was partially supported by FFI/VINNOVA, the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) funded by Knut and Alice Wallenberg Foundation, the Swedish Foundation for Strategic Research (SSF) project Symbicloud, the ELLIIT Excellence Center at Linköping-Lund for Information Technology, Swedish Research Council (VR) Linnaeus Center CADICS, and the National Graduate School in Computer Science, Sweden (CUGS).

Available from: 2018-10-18 Created: 2018-10-18 Last updated: 2019-01-30Bibliographically approved
de Leng, D., Tiger, M., Almquist, M., Almquist, V. & Carlsson, N. (2018). Second Screen Journey to the Cup: Twitter Dynamics during the Stanley Cup Playoffs. In: Proceedings of the 2nd Network Traffic Measurement and Analysis Conference (TMA): . Paper presented at Network Traffic Measurement and Analysis Conference, Vienna, Austria, 26-29 June, 2018 (pp. 1-8).
Open this publication in new window or tab >>Second Screen Journey to the Cup: Twitter Dynamics during the Stanley Cup Playoffs
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2018 (English)In: Proceedings of the 2nd Network Traffic Measurement and Analysis Conference (TMA), 2018, p. 1-8Conference paper, Oral presentation only (Refereed)
Abstract [en]

With Twitter and other microblogging services, users can easily express their opinion and ideas in short text messages. A recent trend is that users use the real-time property of these services to share their opinions and thoughts as events unfold on TV or in the real world. In the context of TV broadcasts, Twitter (over a mobile device, for example) is referred to as a second screen. This paper presents the first characterization of the second screen usage over the playoffs of a major sports league. We present both temporal and spatial analysis of the Twitter usage during the end of the National Hockey League (NHL) regular season and the 2015 Stanley Cup playoffs. Our analysis provides insights into the usage patterns over the full 72-day period and with regards to in-game events such as goals, but also with regards to geographic biases. Quantifying these biases and the significance of specific events, we then discuss and provide insights into how the playoff dynamics may impact advertisers and third-party developers that try to provide increased personalization.

Keywords
Second Screen, Social Media, Twitter, National Hockey League, Personalization
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-148431 (URN)10.23919/TMA.2018.8506531 (DOI)000454696100018 ()978-3-903176-09-6 (ISBN)978-1-5386-7152-8 (ISBN)
Conference
Network Traffic Measurement and Analysis Conference, Vienna, Austria, 26-29 June, 2018
Funder
CUGS (National Graduate School in Computer Science)Swedish Research Council
Note

Funding agencies:  Swedish Research Council (VR); National Graduate School in Computer Science, Sweden (CUGS) Swedish Research Council (VR); National Graduate School in Computer Science, Sweden (CUGS)

Available from: 2018-06-11 Created: 2018-06-11 Last updated: 2019-01-21
Tiger, M. & Heintz, F. (2015). Online Sparse Gaussian Process Regression for Trajectory Modeling. In: 18th International Conference on Information Fusion (Fusion), 2015: . Paper presented at 18th International Conference on Information Fusion (Fusion), 6-9 July, Washington, DC, USA (pp. 782-791). IEEE
Open this publication in new window or tab >>Online Sparse Gaussian Process Regression for Trajectory Modeling
2015 (English)In: 18th International Conference on Information Fusion (Fusion), 2015, IEEE , 2015, p. 782-791Conference paper, Published paper (Refereed)
Abstract [en]

Trajectories are used in many target tracking and other fusion-related applications. In this paper we consider the problem of modeling trajectories as Gaussian processes and learning such models from sets of observed trajectories. We demonstrate that the traditional approach to Gaussian process regression is not suitable when modeling a set of trajectories. Instead we introduce an approach to Gaussian process trajectory regression based on an alternative way of combing two Gaussian process (GP) trajectory models and inverse GP regression. The benefit of our approach is that it works well online and efficiently supports sophisticated trajectory model manipulations such as merging and splitting of trajectory models. Splitting and merging is very useful in spatio-temporal activity modeling and learning where trajectory models are considered discrete objects. The presented method and accompanying approximation algorithm have time and memory complexities comparable to state of the art of regular full and approximative GP regression, while havinga more flexible model suitable for modeling trajectories. The novelty of our approach is in the very flexible and accurate model, especially for trajectories, and the proposed approximative method based on solving the inverse problem of Gaussian process regression.

Place, publisher, year, edition, pages
IEEE, 2015
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-119009 (URN)000389523300103 ()9780982443866 (ISBN)9780982443873 (ISBN)
Conference
18th International Conference on Information Fusion (Fusion), 6-9 July, Washington, DC, USA
Projects
CADICSCENIITCUASCUGSELLIIT
Funder
CUGS (National Graduate School in Computer Science)ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2015-06-07 Created: 2015-06-07 Last updated: 2019-07-03Bibliographically approved
Tiger, M. & Heintz, F. (2015). Towards Unsupervised Learning, Classification and Prediction of Activities in a Stream-Based Framework. In: Proceedings of the Thirteenth Scandinavian Conference on Artificial Intelligence (SCAI): . Paper presented at 13th Scandinavian Conference on Artificial Intelligence, Halmstad, Sweden, November 2015 (pp. 147-156). IOS Press, 278
Open this publication in new window or tab >>Towards Unsupervised Learning, Classification and Prediction of Activities in a Stream-Based Framework
2015 (English)In: Proceedings of the Thirteenth Scandinavian Conference on Artificial Intelligence (SCAI), IOS Press, 2015, Vol. 278, p. 147-156Conference paper, Published paper (Refereed)
Abstract [en]

Learning to recognize common activities such as traffic activities and robot behavior is an important and challenging problem related both to AI and robotics. We propose an unsupervised approach that takes streams of observations of objects as input and learns a probabilistic representation of the observed spatio-temporal activities and their causal relations. The dynamics of the activities are modeled using sparse Gaussian processes and their causal relations using a probabilistic graph. The learned model supports in limited form both estimating the most likely current activity and predicting the most likely future activities.  The framework is evaluated by learning activities in a simulated traffic monitoring application and by learning the flight patterns of an autonomous quadcopter.

Place, publisher, year, edition, pages
IOS Press, 2015
Series
Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389, E-ISSN 1879-8314 ; 278
Keywords
Online Unsupervised Learning, Activity Recognition, Situation Awareness
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-121125 (URN)10.3233/978-1-61499-589-0-147 (DOI)978-1-61499-588-3 (ISBN)
Conference
13th Scandinavian Conference on Artificial Intelligence, Halmstad, Sweden, November 2015
Projects
CADICSCENIITCUASCUGSELLIIT
Funder
CUGS (National Graduate School in Computer Science)ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2015-09-08 Created: 2015-09-08 Last updated: 2019-07-03
Tiger, M. & Heintz, F. (2014). Towards Learning and Classifying Spatio-Temporal Activities in a Stream Processing Framework. In: Ulle Endriss and João Leite (Ed.), STAIRS 2014: Proceedings of the 7th European Starting AI Researcher Symposium. Paper presented at 7th European Starting AI Researcher Symposium (STAIRS-2014), August 18-19, 2014, Prague, Czech Republic (pp. 280-289). IOS Press
Open this publication in new window or tab >>Towards Learning and Classifying Spatio-Temporal Activities in a Stream Processing Framework
2014 (English)In: STAIRS 2014: Proceedings of the 7th European Starting AI Researcher Symposium / [ed] Ulle Endriss and João Leite, IOS Press, 2014, p. 280-289Conference paper, Published paper (Refereed)
Abstract [en]

We propose an unsupervised stream processing framework that learns a Bayesian representation of observed spatio-temporal activities and their causal relations. The dynamics of the activities are modeled using sparse Gaussian processes and their causal relations using a causal Bayesian graph. This allows the model to be efficient through compactness and sparsity in the causal graph, and to provide probabilities at any level of abstraction for activities or chains of activities. Methods and ideas from a wide range of previous work are combined and interact to provide a uniform way to tackle a variety of common problems related to learning, classifying and predicting activities. We discuss how to use this framework to perform prediction of future activities and to generate events.

Place, publisher, year, edition, pages
IOS Press, 2014
Series
Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389, E-ISSN 1879-8314 ; 264
Keywords
activity recognition, machine learning, knowledge representation, situation awareness, knowledge acquisition, unsupervised learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-110570 (URN)10.3233/978-1-61499-421-3-280 (DOI)000350218400029 ()978-1-61499-420-6 (ISBN)978-1-61499-421-3 (ISBN)
Conference
7th European Starting AI Researcher Symposium (STAIRS-2014), August 18-19, 2014, Prague, Czech Republic
Projects
CUASCENIITCADICSELLIIT
Available from: 2014-09-14 Created: 2014-09-14 Last updated: 2018-01-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8546-4431

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