A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Introduction to Computer Graphics
Introduction to Computer Graphics
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
An Automated Method for Large-Scale, Ground-Based City Model Acquisition
International Journal of Computer Vision
Direct Methods for Sparse Linear Systems (Fundamentals of Algorithms 2)
Direct Methods for Sparse Linear Systems (Fundamentals of Algorithms 2)
A constrained SLAM approach to robust and accurate localisation of autonomous ground vehicles
Robotics and Autonomous Systems
Robust and efficient robotic mapping
Robust and efficient robotic mapping
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Nonlinear constraint network optimization for efficient map learning
IEEE Transactions on Intelligent Transportation Systems
Large-scale loop-closing by fusing range data and aerial image
International Journal of Robotics and Automation
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A multilevel relaxation algorithm for simultaneous localization and mapping
IEEE Transactions on Robotics
Self-calibration for a 3D laser
International Journal of Robotics Research
Dense map inference with user-defined priors: from priorlets to scan eigenvariations
SC'12 Proceedings of the 2012 international conference on Spatial Cognition VIII
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The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, most existing solutions to the SLAM problem learn the maps from scratch and have no means for incorporating prior information. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. It inserts correspondences found between stereo and three-dimensional range data and the aerial images as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired even in mixed in- and outdoor environments by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency.