Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
Mobile Robot Localization Using Sonar
IEEE Transactions on Pattern Analysis and Machine Intelligence
Physically Based Simulation Model for Acoustic Sensor Robot Navigation
IEEE Transactions on Pattern Analysis and Machine Intelligence
On active contour models and balloons
CVGIP: Image Understanding
Directed Sonar Sensing for Mobile Robot Navigation
Directed Sonar Sensing for Mobile Robot Navigation
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interpolating scattered data using 2D self-organizing feature maps
Graphical Models
Directional Processing of Ultrasonic Arc Maps and its Comparison with Existing Techniques
International Journal of Robotics Research
Constructing maps for mobile robot navigation based on ultrasonic range data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Efficient energies and algorithms for parametric snakes
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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Active snake contours and Kohonen's self-organizing feature maps (SOMs) are employed for representing and evaluating discrete point maps of indoor environments efficiently and compactly. A generic error criterion is developed for comparing two different sets of points based on the Euclidean distance measure. The point sets can be chosen as (i) two different sets of map points acquired with different mapping techniques or different sensing modalities, (ii) two sets of fitted curve points to maps extracted by different mapping techniques or sensing modalities, or (iii) a set of extracted map points and a set of fitted curve points. The error criterion makes it possible to compare the accuracy of maps obtained with different techniques among themselves, as well as with an absolute reference. Guidelines for selecting and optimizing the parameters of active snake contours and SOMs are provided using uniform sampling of the parameter space and particle swarm optimization (PSO). A demonstrative example from ultrasonic mapping is given based on experimental data and compared with a very accurate laser map, considered an absolute reference. Both techniques can fill the erroneous gaps in discrete point maps. Snake curve fitting results in more accurate maps than SOMs because it is more robust to outliers. The two methods and the error criterion are sufficiently general that they can also be applied to discrete point maps acquired with other mapping techniques and other sensing modalities.