| 1. |
- Andreasson, Henrik, et al.
(författare)
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Non-iterative Vision-based Interpolation of 3D Laser Scans
- 2007
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Ingår i: Autonomous Robots and Agents. - Berlin / Heidelberg : Springer. ; s. 83-90
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Bokkapitel (övrigt vetenskapligt)abstract
- 3D range sensors, particularly 3D laser range scanners, enjoy a rising popularity and are used nowadays for many different applications. The resolution 3D range sensors provide in the image plane is typically much lower than the resolution of a modern colour camera. In this chapter we focus on methods to derive a highresolution depth image from a low-resolution 3D range sensor and a colour image. The main idea is to use colour similarity as an indication of depth similarity, based on the observation that depth discontinuities in the scene often correspond to colour or brightness changes in the camera image. We present five interpolation methods and compare them with an independently proposed method based on Markov random fields. The proposed algorithms are non-iterative and include a parameter-free vision-based interpolation method. In contrast to previous work, we present ground truth evaluation with real world data and analyse both indoor and outdoor data
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| 2. |
- Asadi, Sahar, et al.
(författare)
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Statistical gas distribution modeling using kernel methods
- 2011
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Ingår i: Intelligent systems for machine olfaction : tools and methodologies. - IGI Global. - 13: 9781615209156 ; s. 153-179
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Bokkapitel (övrigt vetenskapligt)abstract
- Gas distribution models can provide comprehensive information about a large number of gas concentration measurements, highlighting, for example, areas of unusual gas accumulation. They can also help to locate gas sources and to plan where future measurements should be carried out. Current physical modeling methods, however, are computationally expensive and not applicable for real world scenarios with real-time and high resolution demands. This chapter reviews kernel methodsthat statistically model gas distribution. Gas measurements are treated as randomvariables, and the gas distribution is predicted at unseen locations either using akernel density estimation or a kernel regression approach. The resulting statistical apmodelsdo not make strong assumptions about the functional form of the gas distribution,such as the number or locations of gas sources, for example. The majorfocus of this chapter is on two-dimensional models that provide estimates for themeans and predictive variances of the distribution. Furthermore, three extensionsto the presented kernel density estimation algorithm are described, which allow toinclude wind information, to extend the model to three dimensions, and to reflecttime-dependent changes of the random process that generates the gas distributionmeasurements. All methods are discussed based on experimental validation usingreal sensor data.
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| 3. |
- Lilienthal, Achim J.
(författare)
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Improved gas source localization with a mobile robot by learning analytical gas dispersal models from statistical gas distribution maps using evolutionary algorithms
- 2011
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Ingår i: Intelligent Systems for Machine Olfaction : Tools and Methodologies. - IGI Global. - 9781615209156 ; s. 249-276
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Bokkapitel (övrigt vetenskapligt)abstract
- The method presented in this chapter computes an estimate of the location of a single gas sourcefrom a set of localised gas sensor measurements. The estimation process consists of three steps.First, a statistical model of the time-averaged gas distribution is estimated in the form of a two-dimensional grid map. In order to compute the gas distribution grid map the Kernel DM algorithm isapplied, which carries out spatial integration by convolving localised sensor readings and modelling theinformation content of the point measurements with a Gaussian kernel. The statistical gas distributiongrid map averages out the transitory effects of turbulence and converges to a representation of thetime-averaged spatial distribution of a target gas. The second step is to learn the parameters ofan analytical model of average gas distribution. Learning is achieved by nonlinear least squaresfitting of the analytical model to the statistical gas distribution map using Evolution Strategies (ES),which are a special type of Evolutionary Algorithms (EA). This step provides an analysis of thestatistical gas distribution map regarding the airflow conditions and an alternative estimate of thegas source location, i.e. the location predicted by the analytical model in addition to the location ofthe maximum in the statistical gas distribution map. In the third step, an improved estimate of thegas source position can then be derived by considering the maximum in the statistical gas distributionmap, the best fit as well as the corresponding fitness value. Different methods to select the mosttruthful estimate are introduced and a comparison regarding their accuracy is presented, based on atotal of 34 hours of gas distribution mapping experiments with a mobile robot. This chapter is anextended version of a paper by the authors (Lilienthal et al. [2005]).
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| 4. |
- Persson, Martin, et al.
(författare)
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Improved mapping and image segmentation by using semantic information to link aerial images and ground-level information
- 2008
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Ingår i: Recent Progress in Robotics: Viable Robotic Service to Human. - Berlin : Springer. - 978-3-540-76728-2 ; s. 157-169
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Bokkapitel (övrigt vetenskapligt)abstract
- This paper investigates the use of semantic information to link ground-level occupancy maps and aerial images. A ground-level semantic map is obtained by a mobile robot equipped with an omnidirectional camera, differential GPS and a laser range finder. The mobile robot uses a virtual sensor for building detection (based on omnidirectional images) to compute the ground-level semantic map, which indicates the probability of the cells being occupied by the wall of a building. These wall estimates from a ground perspective are then matched with edges detected in an aerial image. The result is used to direct a region- and boundary-based segmentation algorithm for building detection in the aerial image. This approach addresses two difficulties simultaneously: 1) the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in monocular aerial images. With the suggested method building outlines can be detected faster than the mobile robot can explore the area by itself, giving the robot an ability to “see” around corners. At the same time, the approach can compensate for the absence of elevation data in segmentation of aerial images. Our experiments demonstrate that ground-level semantic information (wall estimates) allows to focus the segmentation of the aerial image to find buildings and produce a ground-level semantic map that covers a larger area than can be built using the onboard sensors.
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| 5. |
- Reggente, Matteo, et al.
(författare)
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Using local wind information for gas distribution mapping in outdoor environments with a mobile robot
- 2009
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Ingår i: 2009 IEEE SENSORS, VOLS 1-3. - NEW YORK : IEEE. ; s. 1715-1720
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Bokkapitel (övrigt vetenskapligt)abstract
- In this paper we introduce a statistical method to build two-dimensional gas distribution maps (Kernel DM+V/W algorithm). In addition to gas sensor measurements, the proposed method also takes into account wind information by modeling the information content of the gas sensor measurements as a bivariate Gaussian kernel whose shape depends on the measured wind vector. We evaluate the method based on real measurements in an outdoor environment obtained with a mobile robot that was equipped with gas sensors and an ultrasonic anemometer for wind measurements. As a measure of the model quality we compute how well unseen measurements are predicted in terms of the data likelihood. The initial results are encouraging and show a clear improvement of the proposed method compared to the case where wind is not considered.
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