Perceptual organization and the representation of natural form
Artificial Intelligence
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Perception as Bayesian inference
Perception as Bayesian inference
The spatial semantic hierarchy
Artificial Intelligence
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Spatial Cognition and Computation
Fast Line and Rectangle Detection by Clustering and Grouping
CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns
New EM derived from Kullback-Leibler divergence
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Spatial Cognition IVReasoning, Action, Interaction: International Spatial Cognition 2004, Frauenchiemsee, Germany, October 11-13, 2004, Revised Selected ... / Lecture Notes in Artificial Intelligence)
Stanley: The robot that won the DARPA Grand Challenge: Research Articles
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
Multi robot mapping using force field simulation: Research Articles
Journal of Field Robotics
PerMIS '07 Proceedings of the 2007 Workshop on Performance Metrics for Intelligent Systems
DGCI'05 Proceedings of the 12th international conference on Discrete Geometry for Computer Imagery
P-SLAM: Simultaneous Localization and Mapping With Environmental-Structure Prediction
IEEE Transactions on Robotics
Evaluation of maps using fixed shapes: the fiducial map metric
Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop
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In this paper we present a system to enhance the performance of feature correspondence based alignment algorithms for laser scan data. We show how this system can be utilized as a new approach for evaluation of mapping algorithms. Assuming a certain a priori knowledge, our system augments the sensor data with hypotheses (`Virtual Scans') about ideal models of objects in the robot's environment. These hypotheses are generated by analysis of the current aligned map estimated by an underlying iterative alignment algorithm. The augmented data is used to improve the alignment process. Feedback between data alignment and data analysis confirms, modifies, or discards the Virtual Scans in each iteration. Experiments with a simulated scenario and real world data from a rescue robot scenario show the applicability and advantages of the approach. By replacing the estimated `Virtual Scans' with ground truth maps our system can provide a flexible way for evaluating different mapping algorithms in different settings.