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
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
ICP Registration Using Invariant Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
A review of recent range image registration methods with accuracy evaluation
Image and Vision Computing
Metric-based iterative closest point scan matching for sensor displacement estimation
IEEE Transactions on Robotics
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this paper presents a robust laser scan matching algorithm in dynamic environments. Scan matching is thought to be an essential function for mapping and localization of mobile robots. Our method is based on the RANdom Sample and Consensus (RANSAC) algorithm known for its good robust parameter estimation of the model parameters. Different from the existing scan matching methods for mobile robots, we only use the raw data of laser scanning without odometer information to find the transformation between two given laser data sets. Our method does not require any feature extraction and also need not initial estimation to reach global optimum. We demonstrate the practical usability of the proposed approach through Experiment.