Rolf Lakaemper - Academia.edu (original) (raw)

Papers by Rolf Lakaemper

Proceedings of SPIE, Sep 23, 1999

Recently Latecki and Lak amper (Computer Vision and Image Understanding 73:3, March 1999) reporte... more Recently Latecki and Lak amper (Computer Vision and Image Understanding 73:3, March 1999) reported a novel process for a discrete curve evolution. This process has various application possibilities, in particular, for noise removal and shape simpli cation of boundary curves in digital images. In this paper we prove t h a t the process of the discrete curve evolution is continuous: if polygon Q is close to polygon P, then the polygons obtained by their evolution remain close. This result follows directly from the fact that the evolution of Q corresponds to the evolution of P if Q approximates P. This intuitively means that rst all vertices of Q are deleted that are not close to any v ertex of P, and then, whenever a vertex of P is deleted, then a vertex of Q that is close to it is deleted in the corresponding evolution step of Q.

The purpose of this paper is to present a technique to create a global map of robots' surrounding... more The purpose of this paper is to present a technique to create a global map of robots' surroundings by converting the raw data acquired from a scanning sensor to a compact map composed of just a few generalized polylines (polygonal curves). We propose a new approach to merging robots' maps that is composed of a local geometric process of merging similar line segments (termed Discrete Segment Evolution) with a global statistical control process. In the case of single robot, we are able to incrementally build a map showing the environment the robot has traveled through by merging its polygonal map with actual scans. In the case of a robot team, we are able to identify common parts of their partial maps and if common parts are present construct a joint map of the explored environment.

Proceedings of SPIE, Feb 2, 2006

We will provide psychophysical evidence that recognition of parts of object contours is a necessa... more We will provide psychophysical evidence that recognition of parts of object contours is a necessary component of object recognition. It seems to be obvious that the recognition of parts of object contours is performed by applying a partial shape similarity measure to the query contour part and to the known contour parts. The recognition is completed once a sufficiently similar contour part is found in the database of known contour parts. We will derive necessary requirements for any partial shape similarity measure based on this scenario. We will show that existing shape similarity measures do not satisfy these requirements, and propose a new partial shape similarity measure.

Rescue robots operate in disaster areas where odometric information is known to be highly unrelia... more Rescue robots operate in disaster areas where odometric information is known to be highly unreliable. This paper presents a new approach to odometry independent 2D robot mapping. Scans obtained from different robot positions are assembled to a single global map using alignment based on scan intrinsic information only, namely shape features. The approach detects geometric structures in different scans which are similar by shape and aligns the scans accordingly. The detection of shape similarity is the key to generate global maps even of highly cluttered environments and thus the proposed approach is particularly suitable for applications in the field of rescue robots.

International Journal of Computer Vision, Aug 26, 2009

Constructing correspondences between points characterizing one shape with those characterizing an... more Constructing correspondences between points characterizing one shape with those characterizing another is crucial to understanding what the two shapes have in common. These correspondences are the basis for most alignment processes and shape similarity measures. In this paper we use particle filters to establish perceptually correct correspondences between point sets characterizing shapes. Local shape feature descriptors are used to establish the probability that a point on one shape corresponds to a point on the other shape. Global correspondence structures are calculated using additional constraints on domain knowledge. Domain knowledge is characterized by prior distributions which serve to characterize hypotheses about the global relationships between shapes. These hypotheses are formulated online. This means global constraints are learnt during the particle filtering process, which makes the approach especially interesting for applications where global constraints are hard to define a priori. As an example for such a case, experiments demonstrate the performance of our approach on partial shape matching.

We present a system to increase the performance of feature correspondence based alignment algorit... more We present a system to increase the performance of feature correspondence based alignment algorithms for laser scan data. Alignment approaches for robot mapping, like ICP or FFS, perform successfully only under the condition of sufficient overlap of features between individual scans. This condition is often not met, for example in sparsely scanned environments or disaster areas for search and rescue

Springer eBooks, 2008

We present a new similarity measure between a single shape and a shape group as a basis for shape... more We present a new similarity measure between a single shape and a shape group as a basis for shape clustering following the paradigm of context dependent shape comparison: clusters are generated in the context of a reference shape, defined by the query shape it is compared to. Tightly coupled, the distance measure is the basis for a soft k-means like framework to achieve robust clustering. Successful application of the system along with generation of shape prototypes is demonstrated in comparison to latest approaches using elastic deformation.

We consider the problem of elastic matching of time series. We propose an algorithm that determin... more We consider the problem of elastic matching of time series. We propose an algorithm that determines a subsequence of a target time series that best matches a query series. In the proposed algorithm we map the problem of the best matching subsequence to the problem of a cheapest path in a DAG (directed acyclic graph). The proposed approach allows us to also compute the optimal scale and translation of time series values, which is a nontrivial problem in the case of subsequence matching.

Journal of Field Robotics, 2007

This paper describes a novel approach, called Force Field Simulation, to multi robot mapping that... more This paper describes a novel approach, called Force Field Simulation, to multi robot mapping that works under the constraints given in autonomous search and rescue robotics. Extremely poor prealignment, lack of landmarks, and minimal overlap between scans are the main challenges. The presented algorithm solves the alignment problem of such laser scans utilizing a gradient descent approach motivated by physics, namely simulation of movement of masses in gravitational fields, but exchanges laws of physics with constraints given by human perception. Experiments on different real world data sets show the successful application of the algorithm. We also provide an experimental comparison with classical ICP implementation and a Lu/Milios-type alignment algorithm.

The proposed algorithm segments planar structures out of data gained from 3D laser range scanners... more The proposed algorithm segments planar structures out of data gained from 3D laser range scanners, typically used in robotics. The approach first fits planar patches to the dataset, using a new, extended expectation maximization (EM) algorithm. This algorithm solves the classical EM problems of insufficient initialization by iteratively determining the number and positions of patches in a split and merge

Line segment based representation of 2D robot maps is known to have advantages over raw point dat... more Line segment based representation of 2D robot maps is known to have advantages over raw point data or grid based representation gained from laser range scans. It significantly reduces the size of the data set. It also contains higher geometric information, which is necessary for robust post processing. The paper describes an algorithm to convert global 2D robot maps to line segment representation, using a pre-aligned set of point-based single scans as input. Mean-shift clustering on the set of all line segments is utilized to merge perceptually similar segments to single instances: locally linear features in the environment are unambiguously represented by single line segments in the final global map. Apart from a scaling parameter, the approach is parameter free. Experiments on real world data sets prove its applicability in the field of robot mapping.

We present a system for 2D robot mapping which is entirely based on line segment representation o... more We present a system for 2D robot mapping which is entirely based on line segment representation of the environment. The system consists of multiple modules, i.e. scan number reduction, global scan alignment, scan merging and segmenterror filtering, which give an example of the simplicity of mid level data processing and the advanced possibilities opened by segment based design. The compact segment representation enables creation and optimization of a global pose graph for scan registration, which is the core of the mapping system. Experiments verify the applicability to real world data sets and lead to very compact maps, which represent single linear features, e.g. walls, with single line segments.

We present a concept and implementation of a system to integrate low level and mid level spatial ... more We present a concept and implementation of a system to integrate low level and mid level spatial cognition processes for an application in robot mapping. Feedback between the two processes helps to improve performance of the recognition task, in our example the alignment of laser scans. The low level laser range scan data ('real scans'), are analyzed with respect to

IEEE Transactions on Pattern Analysis and Machine Intelligence, Aug 1, 2009

This paper addresses the problem of piecewise linear approximation of point sets without any cons... more This paper addresses the problem of piecewise linear approximation of point sets without any constraints on the order of data points or the number of model components (line segments). We point out two problems with the maximum likelihood estimate (MLE) that present serious drawbacks in practical applications. One is that the parametric models obtained using a classical MLE framework are not guaranteed to be close to data points. It is typically impossible, in this classical framework, to detect whether a parametric model fits the data well or not. The second problem is related to accurately choosing the optimal number of model components. We first fit a nonparametric density to the data points and use it to define a neighborhood of the data. Observations inside this neighborhood are deemed informative; those outside the neighborhood are deemed uninformative for our purpose. This provides us with a means to recognize when models fail to properly fit the data. We then obtain maximum likelihood estimates by optimizing the Kullback-Leibler Divergence (KLD) between the nonparametric data density restricted to this neighborhood and a mixture of parametric models. We prove that, under the assumption of a reasonably large sample size, the inferred model components are close to their ground truth model component counterparts. This holds independently of the initial number of assumed model components or their associated parameters. Moreover, in the proposed approach, we are able to estimate the number of significant model components without any additional computation.

International Conference on Pattern Recognition, Dec 1, 2008

The paper shows how particle filters can be used to establish visually consistent partial corresp... more The paper shows how particle filters can be used to establish visually consistent partial correspondences between similar features in unrestricted 2D point sets representing shapes. Given an update rule, the PF system has the advantage that global constraints can be learned. We motivate and define the update rule for the given task and show its superior performance in comparison to

Springer eBooks, 2005

The purpose of this paper is to present a technique to create a global map of a robot's surroundi... more The purpose of this paper is to present a technique to create a global map of a robot's surrounding by converting the raw data acquired from a scanning sensor to a compact map composed of just a few generalized polylines (polygonal curves). To merge a new scan with a previously computed map of the surrounding we use an approach that is composed of a local geometric process of merging similar line segments (termed Discrete Segment Evolution) of map and scan with a global statistical control process. The merging process is applied to a dataset gained from a real robot to show its ability to incrementally build a map showing the environment the robot has traveled through.

Autonomous Robots, Sep 9, 2009

In this paper we present a system to enhance the performance of feature correspondence based alig... more In this paper we present a system to enhance the performance of feature correspondence based alignment algorithms for laser scan data. We show how this system can be utilized as a new approach for evaluation of mapping algorithms. Assuming a certain a priori knowledge, our system augments the sensor data with hypotheses ('Virtual Scans') about ideal models of objects in the robot's environment. These hypotheses are generated by analysis of the current aligned map estimated by an underlying iterative alignment algorithm. The augmented data is used to improve the alignment process. Feedback between data alignment and data analysis confirms, modifies, or discards the Virtual Scans in each iteration. Experiments with a simulated scenario and real world data from a rescue robot scenario show the applicability and advantages of the approach. By replacing the estimated 'Virtual Scans' with ground truth maps our system can provide a flexible way for evaluating different mapping algorithms in different settings.

Journal of Computing in Civil Engineering, Nov 1, 2009

This paper discusses a framework for integrated augmented reality ͑AR͒ architecture for indoor th... more This paper discusses a framework for integrated augmented reality ͑AR͒ architecture for indoor thermal performance data visualization that utilizes a mobile robot to generate environment maps. It consists of three modules: robot mapping, computational fluid dynamics ͑CFD͒ simulation, and AR visualization. The robot mapping module enables the modeling of spatial geometry using a mobile robot. In order to generate steady approximations to scanned three-dimensional data sets, the paper presents a novel "split-and-merge expectation-maximization patch fitting" ͑SMEMPF͒ planar approximation method. It allows for precise adjustment of patches independent from the initial model. The final result is a set of patches identifying planar macrostructures that consist of a collection of supported tiles. These patches are used to model the spatial geometry under investigation. The CFD simulation module facilitates the prediction of building performance databased on the spatial data generated using the SMEMPF method. The AR visualization module assists in interactive and immersive visualization of CFD simulation results. Such an integrated AR architecture will facilitate rapid multiroom mobile AR visualizations.

We introduce a new EM framework in which it is possible not only to optimize the model parameters... more We introduce a new EM framework in which it is possible not only to optimize the model parameters but also the number of model components. A key feature of our approach is that we use nonparametric density estimation to improve parametric density estimation in the EM framework. While the classical EM algorithm estimates model parameters empirically using the data points themselves, we estimate them using nonparametric density estimates. There exist many possible applications that require optimal adjustment of model components. We present experimental results in two domains. One is polygonal approximation of laser range data, which is an active research topic in robot navigation. The other is grouping of edge pixels to contour boundaries, which still belongs to unsolved problems in computer vision.

International Conference on Pattern Recognition, Dec 1, 2008

Merging local maps, acquired by multiple robots, into a global map, (also known as map merging) i... more Merging local maps, acquired by multiple robots, into a global map, (also known as map merging) is one of the important issues faced by virtually all cooperative exploration techniques. We present a novel and simple solution to the problem of map merging by reducing it to the problem of SLAM of a single "virtual" robot. The individual local maps and their shape information constitute the sensor information for the virtual robot. This approach allows us to adapt the framework of Rao-Blackwellized particle filtering used in SLAM of a single robot for the problem of map merging.

Proceedings of SPIE, Sep 23, 1999

Recently Latecki and Lak amper (Computer Vision and Image Understanding 73:3, March 1999) reporte... more Recently Latecki and Lak amper (Computer Vision and Image Understanding 73:3, March 1999) reported a novel process for a discrete curve evolution. This process has various application possibilities, in particular, for noise removal and shape simpli cation of boundary curves in digital images. In this paper we prove t h a t the process of the discrete curve evolution is continuous: if polygon Q is close to polygon P, then the polygons obtained by their evolution remain close. This result follows directly from the fact that the evolution of Q corresponds to the evolution of P if Q approximates P. This intuitively means that rst all vertices of Q are deleted that are not close to any v ertex of P, and then, whenever a vertex of P is deleted, then a vertex of Q that is close to it is deleted in the corresponding evolution step of Q.

The purpose of this paper is to present a technique to create a global map of robots' surrounding... more The purpose of this paper is to present a technique to create a global map of robots' surroundings by converting the raw data acquired from a scanning sensor to a compact map composed of just a few generalized polylines (polygonal curves). We propose a new approach to merging robots' maps that is composed of a local geometric process of merging similar line segments (termed Discrete Segment Evolution) with a global statistical control process. In the case of single robot, we are able to incrementally build a map showing the environment the robot has traveled through by merging its polygonal map with actual scans. In the case of a robot team, we are able to identify common parts of their partial maps and if common parts are present construct a joint map of the explored environment.

Proceedings of SPIE, Feb 2, 2006

We will provide psychophysical evidence that recognition of parts of object contours is a necessa... more We will provide psychophysical evidence that recognition of parts of object contours is a necessary component of object recognition. It seems to be obvious that the recognition of parts of object contours is performed by applying a partial shape similarity measure to the query contour part and to the known contour parts. The recognition is completed once a sufficiently similar contour part is found in the database of known contour parts. We will derive necessary requirements for any partial shape similarity measure based on this scenario. We will show that existing shape similarity measures do not satisfy these requirements, and propose a new partial shape similarity measure.

Rescue robots operate in disaster areas where odometric information is known to be highly unrelia... more Rescue robots operate in disaster areas where odometric information is known to be highly unreliable. This paper presents a new approach to odometry independent 2D robot mapping. Scans obtained from different robot positions are assembled to a single global map using alignment based on scan intrinsic information only, namely shape features. The approach detects geometric structures in different scans which are similar by shape and aligns the scans accordingly. The detection of shape similarity is the key to generate global maps even of highly cluttered environments and thus the proposed approach is particularly suitable for applications in the field of rescue robots.

International Journal of Computer Vision, Aug 26, 2009

Constructing correspondences between points characterizing one shape with those characterizing an... more Constructing correspondences between points characterizing one shape with those characterizing another is crucial to understanding what the two shapes have in common. These correspondences are the basis for most alignment processes and shape similarity measures. In this paper we use particle filters to establish perceptually correct correspondences between point sets characterizing shapes. Local shape feature descriptors are used to establish the probability that a point on one shape corresponds to a point on the other shape. Global correspondence structures are calculated using additional constraints on domain knowledge. Domain knowledge is characterized by prior distributions which serve to characterize hypotheses about the global relationships between shapes. These hypotheses are formulated online. This means global constraints are learnt during the particle filtering process, which makes the approach especially interesting for applications where global constraints are hard to define a priori. As an example for such a case, experiments demonstrate the performance of our approach on partial shape matching.

We present a system to increase the performance of feature correspondence based alignment algorit... more We present a system to increase the performance of feature correspondence based alignment algorithms for laser scan data. Alignment approaches for robot mapping, like ICP or FFS, perform successfully only under the condition of sufficient overlap of features between individual scans. This condition is often not met, for example in sparsely scanned environments or disaster areas for search and rescue

Springer eBooks, 2008

We present a new similarity measure between a single shape and a shape group as a basis for shape... more We present a new similarity measure between a single shape and a shape group as a basis for shape clustering following the paradigm of context dependent shape comparison: clusters are generated in the context of a reference shape, defined by the query shape it is compared to. Tightly coupled, the distance measure is the basis for a soft k-means like framework to achieve robust clustering. Successful application of the system along with generation of shape prototypes is demonstrated in comparison to latest approaches using elastic deformation.

We consider the problem of elastic matching of time series. We propose an algorithm that determin... more We consider the problem of elastic matching of time series. We propose an algorithm that determines a subsequence of a target time series that best matches a query series. In the proposed algorithm we map the problem of the best matching subsequence to the problem of a cheapest path in a DAG (directed acyclic graph). The proposed approach allows us to also compute the optimal scale and translation of time series values, which is a nontrivial problem in the case of subsequence matching.

Journal of Field Robotics, 2007

This paper describes a novel approach, called Force Field Simulation, to multi robot mapping that... more This paper describes a novel approach, called Force Field Simulation, to multi robot mapping that works under the constraints given in autonomous search and rescue robotics. Extremely poor prealignment, lack of landmarks, and minimal overlap between scans are the main challenges. The presented algorithm solves the alignment problem of such laser scans utilizing a gradient descent approach motivated by physics, namely simulation of movement of masses in gravitational fields, but exchanges laws of physics with constraints given by human perception. Experiments on different real world data sets show the successful application of the algorithm. We also provide an experimental comparison with classical ICP implementation and a Lu/Milios-type alignment algorithm.

The proposed algorithm segments planar structures out of data gained from 3D laser range scanners... more The proposed algorithm segments planar structures out of data gained from 3D laser range scanners, typically used in robotics. The approach first fits planar patches to the dataset, using a new, extended expectation maximization (EM) algorithm. This algorithm solves the classical EM problems of insufficient initialization by iteratively determining the number and positions of patches in a split and merge

Line segment based representation of 2D robot maps is known to have advantages over raw point dat... more Line segment based representation of 2D robot maps is known to have advantages over raw point data or grid based representation gained from laser range scans. It significantly reduces the size of the data set. It also contains higher geometric information, which is necessary for robust post processing. The paper describes an algorithm to convert global 2D robot maps to line segment representation, using a pre-aligned set of point-based single scans as input. Mean-shift clustering on the set of all line segments is utilized to merge perceptually similar segments to single instances: locally linear features in the environment are unambiguously represented by single line segments in the final global map. Apart from a scaling parameter, the approach is parameter free. Experiments on real world data sets prove its applicability in the field of robot mapping.

We present a system for 2D robot mapping which is entirely based on line segment representation o... more We present a system for 2D robot mapping which is entirely based on line segment representation of the environment. The system consists of multiple modules, i.e. scan number reduction, global scan alignment, scan merging and segmenterror filtering, which give an example of the simplicity of mid level data processing and the advanced possibilities opened by segment based design. The compact segment representation enables creation and optimization of a global pose graph for scan registration, which is the core of the mapping system. Experiments verify the applicability to real world data sets and lead to very compact maps, which represent single linear features, e.g. walls, with single line segments.

We present a concept and implementation of a system to integrate low level and mid level spatial ... more We present a concept and implementation of a system to integrate low level and mid level spatial cognition processes for an application in robot mapping. Feedback between the two processes helps to improve performance of the recognition task, in our example the alignment of laser scans. The low level laser range scan data ('real scans'), are analyzed with respect to

IEEE Transactions on Pattern Analysis and Machine Intelligence, Aug 1, 2009

This paper addresses the problem of piecewise linear approximation of point sets without any cons... more This paper addresses the problem of piecewise linear approximation of point sets without any constraints on the order of data points or the number of model components (line segments). We point out two problems with the maximum likelihood estimate (MLE) that present serious drawbacks in practical applications. One is that the parametric models obtained using a classical MLE framework are not guaranteed to be close to data points. It is typically impossible, in this classical framework, to detect whether a parametric model fits the data well or not. The second problem is related to accurately choosing the optimal number of model components. We first fit a nonparametric density to the data points and use it to define a neighborhood of the data. Observations inside this neighborhood are deemed informative; those outside the neighborhood are deemed uninformative for our purpose. This provides us with a means to recognize when models fail to properly fit the data. We then obtain maximum likelihood estimates by optimizing the Kullback-Leibler Divergence (KLD) between the nonparametric data density restricted to this neighborhood and a mixture of parametric models. We prove that, under the assumption of a reasonably large sample size, the inferred model components are close to their ground truth model component counterparts. This holds independently of the initial number of assumed model components or their associated parameters. Moreover, in the proposed approach, we are able to estimate the number of significant model components without any additional computation.

International Conference on Pattern Recognition, Dec 1, 2008

The paper shows how particle filters can be used to establish visually consistent partial corresp... more The paper shows how particle filters can be used to establish visually consistent partial correspondences between similar features in unrestricted 2D point sets representing shapes. Given an update rule, the PF system has the advantage that global constraints can be learned. We motivate and define the update rule for the given task and show its superior performance in comparison to

Springer eBooks, 2005

The purpose of this paper is to present a technique to create a global map of a robot's surroundi... more The purpose of this paper is to present a technique to create a global map of a robot's surrounding by converting the raw data acquired from a scanning sensor to a compact map composed of just a few generalized polylines (polygonal curves). To merge a new scan with a previously computed map of the surrounding we use an approach that is composed of a local geometric process of merging similar line segments (termed Discrete Segment Evolution) of map and scan with a global statistical control process. The merging process is applied to a dataset gained from a real robot to show its ability to incrementally build a map showing the environment the robot has traveled through.

Autonomous Robots, Sep 9, 2009

In this paper we present a system to enhance the performance of feature correspondence based alig... more In this paper we present a system to enhance the performance of feature correspondence based alignment algorithms for laser scan data. We show how this system can be utilized as a new approach for evaluation of mapping algorithms. Assuming a certain a priori knowledge, our system augments the sensor data with hypotheses ('Virtual Scans') about ideal models of objects in the robot's environment. These hypotheses are generated by analysis of the current aligned map estimated by an underlying iterative alignment algorithm. The augmented data is used to improve the alignment process. Feedback between data alignment and data analysis confirms, modifies, or discards the Virtual Scans in each iteration. Experiments with a simulated scenario and real world data from a rescue robot scenario show the applicability and advantages of the approach. By replacing the estimated 'Virtual Scans' with ground truth maps our system can provide a flexible way for evaluating different mapping algorithms in different settings.

Journal of Computing in Civil Engineering, Nov 1, 2009

This paper discusses a framework for integrated augmented reality ͑AR͒ architecture for indoor th... more This paper discusses a framework for integrated augmented reality ͑AR͒ architecture for indoor thermal performance data visualization that utilizes a mobile robot to generate environment maps. It consists of three modules: robot mapping, computational fluid dynamics ͑CFD͒ simulation, and AR visualization. The robot mapping module enables the modeling of spatial geometry using a mobile robot. In order to generate steady approximations to scanned three-dimensional data sets, the paper presents a novel "split-and-merge expectation-maximization patch fitting" ͑SMEMPF͒ planar approximation method. It allows for precise adjustment of patches independent from the initial model. The final result is a set of patches identifying planar macrostructures that consist of a collection of supported tiles. These patches are used to model the spatial geometry under investigation. The CFD simulation module facilitates the prediction of building performance databased on the spatial data generated using the SMEMPF method. The AR visualization module assists in interactive and immersive visualization of CFD simulation results. Such an integrated AR architecture will facilitate rapid multiroom mobile AR visualizations.

We introduce a new EM framework in which it is possible not only to optimize the model parameters... more We introduce a new EM framework in which it is possible not only to optimize the model parameters but also the number of model components. A key feature of our approach is that we use nonparametric density estimation to improve parametric density estimation in the EM framework. While the classical EM algorithm estimates model parameters empirically using the data points themselves, we estimate them using nonparametric density estimates. There exist many possible applications that require optimal adjustment of model components. We present experimental results in two domains. One is polygonal approximation of laser range data, which is an active research topic in robot navigation. The other is grouping of edge pixels to contour boundaries, which still belongs to unsolved problems in computer vision.

International Conference on Pattern Recognition, Dec 1, 2008

Merging local maps, acquired by multiple robots, into a global map, (also known as map merging) i... more Merging local maps, acquired by multiple robots, into a global map, (also known as map merging) is one of the important issues faced by virtually all cooperative exploration techniques. We present a novel and simple solution to the problem of map merging by reducing it to the problem of SLAM of a single "virtual" robot. The individual local maps and their shape information constitute the sensor information for the virtual robot. This approach allows us to adapt the framework of Rao-Blackwellized particle filtering used in SLAM of a single robot for the problem of map merging.