Tracking and data association
Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Active vision
An autonomous guided vehicle for cargo handling applications
International Journal of Robotics Research
SCAAT: incremental tracking with incomplete information
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
VIS-Tracker: A Wearable Vision-Inertial Self-Tracker
VR '03 Proceedings of the IEEE Virtual Reality 2003
Vision-based rov system
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures
International Journal of Robotics Research
A probabilistic framework for entire WSN localization using a mobile robot
Robotics and Autonomous Systems
Information-based compact pose SLAM
IEEE Transactions on Robotics
Sequential covariance intersection fusion Kalman filter
Information Sciences: an International Journal
Graph-based distributed cooperative navigation for a general multi-robot measurement model
International Journal of Robotics Research
PCRLB-based sensor selection for maneuvering target tracking in range-based sensor networks
Future Generation Computer Systems
Line point registration: a technique for enhancing robot localization in a soccer environment
Robot Soccer World Cup XV
International Journal of Sensor Networks
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One of the greatest obstacles to the use of Simultaneous Localization And Mapping (SLAM) in a real-world environment is the need to maintain the full correlation structure between the vehicle and all of the landmark estimates. This structure is computationally expensive to maintain and is not robust to linearization errors. In this tutorial we describe SLAM algorithms that attempt to circumvent these difficulties through the use of Covariance Intersection (CI). CI is the optimal algorithm for fusing estimates when the correlations among them are unknown. A feature of CI relative to techniques which exploit full correlation information is that it provides provable consistency with much less computational overhead. In practice, however, a tradeoff typically needs to be made between estimation accuracy and computational cost. We describe a number of techniques that span the range of tradeoffs from maximum computational efficiency with straight CI to maximum estimation efficiency with the maintenance of all correlation information. We present a set of examples illustrating benefits of CI-based SLAM.