Digital Image Registration Using Projections
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of image registration techniques
ACM Computing Surveys (CSUR)
The map-building and exploration strategies of a simple sonar-equipped mobile robot
The map-building and exploration strategies of a simple sonar-equipped mobile robot
Efficiently Locating Objects Using the Hausdorff Distance
International Journal of Computer Vision
Registration and Integration of Multiple Object Views for 3D Model Construction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Directed Sonar Sensing for Mobile Robot Navigation
Directed Sonar Sensing for Mobile Robot Navigation
Proceedings of the 2001 conference on Virtual reality, archeology, and cultural heritage
Affine/ Photometric Invariants for Planar Intensity Patterns
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Exploring artificial intelligence in the new millennium
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Distributed Cooperative Outdoor Multirobot Localization and Mapping
Autonomous Robots
A Discussion of Simultaneous Localization and Mapping
Autonomous Robots
Fast and accurate map merging for multi-robot systems
Autonomous Robots
Determining Map Quality through an Image Similarity Metric
RoboCup 2008: Robot Soccer World Cup XII
Motion planning using adaptive random walks
IEEE Transactions on Robotics
Evaluation of maps using fixed shapes: the fiducial map metric
Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop
A hybrid approach to 2D robotic map evaluation
Proceedings of the Workshop on Performance Metrics for Intelligent Systems
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Various common error sources affect the quality of a map, e.g., salt and pepper noise and other forms of noise that are more or less uniformly distributed over the map. But there also exist errors that only occur very rarely in the mapping process but that have severe effects on the final result. They influence not only the local accuracy but also the whole spatial layout of the map. Examples of related error sources include bump noise in the robot's pose or residual errors in Simultaneous Localization and Mapping (SLAM). The concept of brokenness is introduced in this article to capture the notion of structural errors in grid maps. The map is partitioned into regions that are locally consistent with ground truth but "off" relative to each other. Brokenness measures the number of these regions and their spatial relations. A theoretical basis is introduced to derive the concept of brokenness in a formal way. Furthermore, it is shown how brokenness can be computed in an algorithmic way. Experiments with maps from simulated as well as real world data are presented. They show that the metric can indeed be used to automatically determine the structural quality of a map in a quantitative way.