A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
An Automated Method for Large-Scale, Ground-Based City Model Acquisition
International Journal of Computer Vision
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
A comparison of SLAM algorithms based on a graph of relations
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Large-scale loop-closing by fusing range data and aerial image
International Journal of Robotics and Automation
RoboCupRescue Interleague Challenge 2009: bridging the gap between simulation and reality
PerMIS '09 Proceedings of the 9th Workshop on Performance Metrics for Intelligent Systems
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
IEEE Transactions on Robotics
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In critical domains such as urban search and rescue (USAR), and bomb disposal, the deployment of teleoperated robots is essential to reduce the risk of first responder personnel. Teleoperation is a difficult task, particularly when controlling robots from an isolated safety zone. In general, the operator has to solve simultaneously the problems of mission planning, target identification, robot navigation, and robot control. We introduce a system to support teleoperated navigation with real-time mapping consisting of a two-step scan matching method that re-considers data associations during the search over transformations. The algorithm processes data from laser range finder and gyroscope only, thereby it is independent from the robot platform. Furthermore, we introduce a user-guided procedure for improving the global consistency of maps generated by the scan matcher. Globally consistent maps are computed by a graph-based maximum likelihood method that is biased by localizing crucial parts of the scan matcher trajectory on a prior given geo-tiff image. The approach has been implemented as an embedded system and extensively tested on robot platforms designed for teleoperation in critical situations, such as bomb disposal. Furthermore, the system was evaluated in a test maze by first responders during the Disaster City event in Texas, 2008.