Computational geometry: an introduction
Computational geometry: an introduction
Blanche: position estimation for an autonomous robot vehicle
Autonomous robot vehicles
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Machine vision
The robot localization problem
WAFR Proceedings of the workshop on Algorithmic foundations of robotics
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
How Easy is Matching 2D Line Models Using Local Search?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bias in Robust Estimation Caused by Discontinuities and Multiple Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
Journal of Intelligent and Robotic Systems
Echzeitfähige Merkmalsextraktion und Situationsinterpretation
Autonome Mobile Systeme 1996, 12. Fachgespräch
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
Line Extraction in 2D Range Images for Mobile Robotics
Journal of Intelligent and Robotic Systems
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The celebrated minimum inertia line problem is reconsidered: a line is to be fitted to a planar cloud of points so that the sum of squared distances of all points to the line becomes minimal. The classical algebraic solution based on the tensor of inertia is complemented by a closed form trigonometric solution allowing various generalizations including the fit of elastic polygons. Proper polygons will be fitted numerically with non-closed partial solutions being reduced to the lowest dimension possible. This is complemented by segmentation heuristics for the measurement cloud.The approach allows to solve the robot localization problem with high accuracy for position and orientation to be inferred from distance measurements in a known polygonal environment. The essential feature of the current approach is to fit the polygonal geometry as a whole.