AMIRoLoS an active marker internet-based robot localization system
Robotics and Autonomous Systems
Predicting the Performance of Cooperative Simultaneous Localization and Mapping (C-SLAM)
International Journal of Robotics Research
Monocular Vision for Mobile Robot Localization and Autonomous Navigation
International Journal of Computer Vision
Reconfigurable on-board vision processing for small autonomous vehicles
EURASIP Journal on Embedded Systems
Indoor sound field feature matching for robot's location and orientation detection
Pattern Recognition Letters
Globally consistent 3D mapping with scan matching
Robotics and Autonomous Systems
Towards semantic maps for mobile robots
Robotics and Autonomous Systems
6D SLAM with cached K-D tree search
RA '07 Proceedings of the 13th IASTED International Conference on Robotics and Applications
Intelligent vehicle localization using gps, compass, and machine vision
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Tightly-coupled GIS data in GNSS fix computations with integrity testing
International Journal of Intelligent Information and Database Systems
Optimizing trajectories of mobile beacons to localize sensor networks
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
Evolutionary constrained self-localization for autonomous agents
Applied Soft Computing
Heuristic-based laser scan matching for outdoor 6d SLAM
KI'05 Proceedings of the 28th annual German conference on Advances in Artificial Intelligence
3D mapping with semantic knowledge
RoboCup 2005
Virtual 3D City Model for Navigation in Urban Areas
Journal of Intelligent and Robotic Systems
How the Location of the Range Sensor Affects EKF-based Localization
Journal of Intelligent and Robotic Systems
Building facade detection, segmentation, and parameter estimation for mobile robot stereo vision
Image and Vision Computing
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This paper addresses the problems of building a functional mobile robot for urban site navigation and modeling with focus on keeping track of the robot location. We have developed a localization system that employs two methods. The first method uses odometry, a compass and tilt sensor, and a global positioning sensor. An extended Kalman filter integrates the sensor data and keeps track of the uncertainty associated with it. The second method is based on camera pose estimation. It is used when the uncertainty from the first method becomes very large. The pose estimation is done by matching linear features in the image with a simple and compact environmental model. We have demonstrated the functionality of the robot and the localization methods with real-world experiments.