Incremental Topological Mapping Using Omnidirectional Vision

Valgren, Christoffer; Lilienthal, Achim J.; Duckett, Tom

doi:10.1109/iros.2006.282583

Cited by 25 publications

(17 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A related and robust area of research is topological visual mapping, usually using omnidirectional cameras ( [26], [27], [12] among many). As with laser mapping, there have been few attempts to deal with changing environments.…”

Section: Related Workmentioning

confidence: 99%

Towards lifelong visual maps

Konolige

Bowman

2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

129

105

View full text Add to dashboard Cite

Abstract-The typical SLAM mapping system assumes a static environment and constructs a map that is then used without regard for ongoing changes. Most SLAM systems, such as FastSLAM, also require a single connected run to create a map. In this paper we present a system of visual mapping, using only input from a stereo camera, that continually updates an optimized metric map in large indoor spaces with movable objects: people, furniture, partitions, etc. The system can be stopped and restarted at arbitrary disconnected points, is robust to occlusion and localization failures, and efficiently maintains alternative views of a dynamic environment. It operates completely online at a 30 Hz frame rate.

show abstract

Section: Related Workmentioning

confidence: 99%

Towards lifelong visual maps

Konolige

Bowman

2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

129

105

View full text Add to dashboard Cite

show abstract

“…However, as long as the environment is not extreme in some way (i.e. it is completely featureless or contains repeating patterns), one value is usually sufficient for the entire data set [15].…”

Section: B Similarity Threshold and The Scaling Parameter σmentioning

confidence: 99%

Incremental Spectral Clustering and Its Application To Topological Mapping

Valgren

Duckett

Lilienthal

2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

Self Cite

View full text Add to dashboard Cite

Abstract-This paper presents a novel use of spectral clustering algorithms to support cases where the entries in the affinity matrix are costly to compute. The method is incremental -the spectral clustering algorithm is applied to the affinity matrix after each row/column is added -which makes it possible to inspect the clusters as new data points are added. The method is well suited to the problem of appearance-based, on-line topological mapping for mobile robots. In this problem domain, we show that we can reduce environment-dependent parameters of the clustering algorithm to just a single, intuitive parameter. Experimental results in large outdoor and indoor environments show that we can close loops correctly by computing only a fraction of the entries in the affinity matrix. The accompanying video clip shows how an example map is produced by the algorithm.

show abstract

“…An examination of the literature on visual mobile robot localization and mapping reveals two main approaches: metric methods [1,2], which aim to estimate and track the absolute position of a robot within a geometric map, and appearance-based topological methods [3,4], which represent the environment as a graph where the nodes of this graph correspond to places in the real environment. Between these two main branches, there is a hybrid approach [5].…”

Section: Introductionmentioning

confidence: 99%

Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Dayoub

Cielniak

Duckett

2011

Robotics and Autonomous Systems

Self Cite

View full text Add to dashboard Cite

Real-world environments such as houses and offices change over time, meaning that a mobile robot's map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metrictopological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability.

show abstract

Incremental Topological Mapping Using Omnidirectional Vision

Cited by 25 publications

References 14 publications

Towards lifelong visual maps

Towards lifelong visual maps

Incremental Spectral Clustering and Its Application To Topological Mapping

Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Contact Info

Product

Resources

About