This paper addresses the computation of the fundamental matrix between two views, when camera mot... more This paper addresses the computation of the fundamental matrix between two views, when camera motion and 3D structure are unknown, but planar surfaces can be assumed. We use line features which are automatically matched in two steps. Firstly, with image based parameters, a set of matches are obtained to secondly compute homographies, which allows to reject wrong ones, and to grow good matches in a final stage. The inclusion of projective transformations gives much better results to match features with short computing overload. When two or more planes are observed, different homographies can be computed, segmenting simultaneously the corresponding planar surfaces. These can be used to obtain the fundamental matrix, which gives constraints for the whole scene. The results show that the global process is robust enough, turning out stable and useful to obtain matches and epipolar geometry from lines in man made environments.
An important component of human-robot interaction is the capability to associate semantic concept... more An important component of human-robot interaction is the capability to associate semantic concepts with encountered locations and objects. This functionality is essential for visually guided navigation as well as location and object recognition. In this paper we focus on the problem of door detection using visual information only. Doors are frequently encountered in structured man-made environments and function as transitions between different places. We adopt a probabilistic approach for door detection, by defining the likelihood of various features for generated door hypotheses. Differing from previous approaches, the proposed model captures both the shape and appearance of the door. This is learned from a few training examples, exploiting additional assumptions about the structure of indoor environments. After the learning stage, we describe a hypothesis generation process and several approaches to evaluate the likelihood of the generated hypotheses. The approach is tested on numerous examples of indoor environment. It shows a good performance provided that the door extent in the images is sufficiently large and well supported by low level feature measurements.
Many robotic applications work with visual reference maps, which usually consist of sets of more ... more Many robotic applications work with visual reference maps, which usually consist of sets of more or less organized images. In these applications, there is a compromise between the density of reference data stored and the capacity to identify later the robot localization, when it is not exactly in the same position as one of the reference views. Here we propose the use of a recently developed feature, SURF, to improve the performance of appearance-based localization methods that perform image retrieval in large data sets. This feature is integrated with a vision-based algorithm that allows both topological and metric localization using omnidirectional images in a hierarchical approach. It uses Pyramidal kernels for the topological localization and three-view geometric constraints for the metric one. Experiments with several omnidirectional images sets are shown, including comparisons with other typically used features (radial lines and SIFT). The advantages of this approach are proved, showing the use of SURF as the best compromise between efficiency and accuracy in the results.
In this paper we propose a new vision based method for robot localization using an omnidirectiona... more In this paper we propose a new vision based method for robot localization using an omnidirectional camera. The method has three steps efficiently combined to deal with big reference image sets, each step evaluates less images than the previous but is more complex and accurate. Given the current uncalibrated image seen by the robot, the hierarchical algorithm gives the possibility of obtaining appearance-based (topological) and metric localization. Compared to other similar vision-based localization methods, the one proposed here has the advantage that it gets accurate metric localization based on a minimal reference image set, using the 1D three view geometry. Moreover, thanks to the linear wide baseline features used, the method is insensitive to illumination changes and occlusions, while keeping the computational load small. The simplicity of the radial line feature used speeds up the process while it keeps acceptable accuracy. We show experiments with two omnidirectional image data-sets to evaluate performance of the method.
This paper addresses the robot and landmark localization problem from bearing-only data in three ... more This paper addresses the robot and landmark localization problem from bearing-only data in three views, simultaneously to the robust association of this data. The localization algorithm is based on the 1D trifocal tensor, which relates linearly the observed data and the robot localization parameters. The aim of this work is to bring this useful geometric construction from computer vision closer to robotic applications. One contribution is the evaluation of two linear approaches of estimating the 1D tensor: the commonly used approach that needs seven bearing-only correspondences and another one which uses only five correspondences plus two calibration constraints. The results in this paper show that the inclusion of these constraints provides a simpler and faster solution and better estimation of robot and landmark locations in the presence of noise. Moreover, a new method that makes use of scene planes and requires only four correspondences is presented. This proposal improves the performance of the two previously mentioned methods in typical man-made scenarios with dominant planes, while it gives similar results in other cases. The three methods are evaluated with simulation tests as well as with experiments that perform automatic real data matching in conventional and omnidirectional images. The results show sufficient accuracy and stability to be used in robotic tasks such as navigation, global localization or initialization of SLAM algorithms.
We propose a new vision-based method for global robot localization using an omnidirectional camer... more We propose a new vision-based method for global robot localization using an omnidirectional camera. Topological and metric localization information are combined in an efficient, hierarchical process, with each step more complex and accurate than the previous one but evaluating less images. This allows to work with large reference image sets in a reasonable amount of time. Simultaneously, thanks to the use of 1D three view geometry, accurate metric localization can be achieved based on just a small number of nearby reference images. Thanks to the wide baseline features used, the method deals well with illumination changes and occlusions, while keeping the computational load small. The simplicity of the radial line features used speeds up the process without affecting the accuracy too much. We show experiments with two omnidirectional image data sets to evaluate the performance of the method and compare the results using the proposed radial lines with results from state-of-the-art wide-baseline matching techniques.
In this paper we present a technique to linearly recover 2D structure and motion in man made envi... more In this paper we present a technique to linearly recover 2D structure and motion in man made environments from three uncalibrated omnidirectional views. We use vertical lines from the scene which are projected as radial lines in the images and are automatically matched. The algorithm is based on a 1D radial trifocal tensor which encodes the relations of the three views and the projected lines. We include experiments with real images, which demonstrate the good performance of the method and its application to robotic tasks, such as robot localization based in a database of reference images or to obtain the initial values of robot and landmarks localization in SLAM algorithms.
Nowadays we frequently find big amounts of data to work with, what facilitates many robotic tasks... more Nowadays we frequently find big amounts of data to work with, what facilitates many robotic tasks and helps to solve perception problems. At the same time, this fact origins an interesting ongoing research problem: how to organize and arrange big sets of information to be useful in later uses. Topological mapping is a very useful tool to arrange and deal with big amounts of reference images for robotic tasks. There are many previous works on topological mapping and many others use this kind of maps for topological localization, planning and navigation. This work is focused on the problem of carefully design topological map building processes that facilitate the posterior robot tasks that use them and make them safer. We propose a new hierarchy of topological maps focused on this aspect. The experiments included in this paper were run outdoors using omnidirectional images and GPS information, and show the good topological maps obtained and how they allow robust and safer localization and navigation tasks.
This paper addresses the computation of the fundamental matrix between two views, when camera mot... more This paper addresses the computation of the fundamental matrix between two views, when camera motion and 3D structure are unknown, but planar surfaces can be assumed. We use line features which are automatically matched in two steps. Firstly, with image based parameters, a set of matches are obtained to secondly compute homographies, which allows to reject wrong ones, and to grow good matches in a final stage. The inclusion of projective transformations gives much better results to match features with short computing overload. When two or more planes are observed, different homographies can be computed, segmenting simultaneously the corresponding planar surfaces. These can be used to obtain the fundamental matrix, which gives constraints for the whole scene. The results show that the global process is robust enough, turning out stable and useful to obtain matches and epipolar geometry from lines in man made environments.
An important component of human-robot interaction is the capability to associate semantic concept... more An important component of human-robot interaction is the capability to associate semantic concepts with encountered locations and objects. This functionality is essential for visually guided navigation as well as location and object recognition. In this paper we focus on the problem of door detection using visual information only. Doors are frequently encountered in structured man-made environments and function as transitions between different places. We adopt a probabilistic approach for door detection, by defining the likelihood of various features for generated door hypotheses. Differing from previous approaches, the proposed model captures both the shape and appearance of the door. This is learned from a few training examples, exploiting additional assumptions about the structure of indoor environments. After the learning stage, we describe a hypothesis generation process and several approaches to evaluate the likelihood of the generated hypotheses. The approach is tested on numerous examples of indoor environment. It shows a good performance provided that the door extent in the images is sufficiently large and well supported by low level feature measurements.
Many robotic applications work with visual reference maps, which usually consist of sets of more ... more Many robotic applications work with visual reference maps, which usually consist of sets of more or less organized images. In these applications, there is a compromise between the density of reference data stored and the capacity to identify later the robot localization, when it is not exactly in the same position as one of the reference views. Here we propose the use of a recently developed feature, SURF, to improve the performance of appearance-based localization methods that perform image retrieval in large data sets. This feature is integrated with a vision-based algorithm that allows both topological and metric localization using omnidirectional images in a hierarchical approach. It uses Pyramidal kernels for the topological localization and three-view geometric constraints for the metric one. Experiments with several omnidirectional images sets are shown, including comparisons with other typically used features (radial lines and SIFT). The advantages of this approach are proved, showing the use of SURF as the best compromise between efficiency and accuracy in the results.
In this paper we propose a new vision based method for robot localization using an omnidirectiona... more In this paper we propose a new vision based method for robot localization using an omnidirectional camera. The method has three steps efficiently combined to deal with big reference image sets, each step evaluates less images than the previous but is more complex and accurate. Given the current uncalibrated image seen by the robot, the hierarchical algorithm gives the possibility of obtaining appearance-based (topological) and metric localization. Compared to other similar vision-based localization methods, the one proposed here has the advantage that it gets accurate metric localization based on a minimal reference image set, using the 1D three view geometry. Moreover, thanks to the linear wide baseline features used, the method is insensitive to illumination changes and occlusions, while keeping the computational load small. The simplicity of the radial line feature used speeds up the process while it keeps acceptable accuracy. We show experiments with two omnidirectional image data-sets to evaluate performance of the method.
This paper addresses the robot and landmark localization problem from bearing-only data in three ... more This paper addresses the robot and landmark localization problem from bearing-only data in three views, simultaneously to the robust association of this data. The localization algorithm is based on the 1D trifocal tensor, which relates linearly the observed data and the robot localization parameters. The aim of this work is to bring this useful geometric construction from computer vision closer to robotic applications. One contribution is the evaluation of two linear approaches of estimating the 1D tensor: the commonly used approach that needs seven bearing-only correspondences and another one which uses only five correspondences plus two calibration constraints. The results in this paper show that the inclusion of these constraints provides a simpler and faster solution and better estimation of robot and landmark locations in the presence of noise. Moreover, a new method that makes use of scene planes and requires only four correspondences is presented. This proposal improves the performance of the two previously mentioned methods in typical man-made scenarios with dominant planes, while it gives similar results in other cases. The three methods are evaluated with simulation tests as well as with experiments that perform automatic real data matching in conventional and omnidirectional images. The results show sufficient accuracy and stability to be used in robotic tasks such as navigation, global localization or initialization of SLAM algorithms.
We propose a new vision-based method for global robot localization using an omnidirectional camer... more We propose a new vision-based method for global robot localization using an omnidirectional camera. Topological and metric localization information are combined in an efficient, hierarchical process, with each step more complex and accurate than the previous one but evaluating less images. This allows to work with large reference image sets in a reasonable amount of time. Simultaneously, thanks to the use of 1D three view geometry, accurate metric localization can be achieved based on just a small number of nearby reference images. Thanks to the wide baseline features used, the method deals well with illumination changes and occlusions, while keeping the computational load small. The simplicity of the radial line features used speeds up the process without affecting the accuracy too much. We show experiments with two omnidirectional image data sets to evaluate the performance of the method and compare the results using the proposed radial lines with results from state-of-the-art wide-baseline matching techniques.
In this paper we present a technique to linearly recover 2D structure and motion in man made envi... more In this paper we present a technique to linearly recover 2D structure and motion in man made environments from three uncalibrated omnidirectional views. We use vertical lines from the scene which are projected as radial lines in the images and are automatically matched. The algorithm is based on a 1D radial trifocal tensor which encodes the relations of the three views and the projected lines. We include experiments with real images, which demonstrate the good performance of the method and its application to robotic tasks, such as robot localization based in a database of reference images or to obtain the initial values of robot and landmarks localization in SLAM algorithms.
Nowadays we frequently find big amounts of data to work with, what facilitates many robotic tasks... more Nowadays we frequently find big amounts of data to work with, what facilitates many robotic tasks and helps to solve perception problems. At the same time, this fact origins an interesting ongoing research problem: how to organize and arrange big sets of information to be useful in later uses. Topological mapping is a very useful tool to arrange and deal with big amounts of reference images for robotic tasks. There are many previous works on topological mapping and many others use this kind of maps for topological localization, planning and navigation. This work is focused on the problem of carefully design topological map building processes that facilitate the posterior robot tasks that use them and make them safer. We propose a new hierarchy of topological maps focused on this aspect. The experiments included in this paper were run outdoors using omnidirectional images and GPS information, and show the good topological maps obtained and how they allow robust and safer localization and navigation tasks.
Uploads
Papers by ana murillo