Tracking and data association
An autonomous guided vehicle for cargo handling applications
International Journal of Robotics Research
Directed Sonar Sensing for Mobile Robot Navigation
Directed Sonar Sensing for Mobile Robot Navigation
Sensor Influence in the Performance of Simultaneous Mobile Robot Localization and Map Building
The Sixth International Symposium on Experimental Robotics VI
Experimental Analysis of Adaptive Concurrent Mapping and Localization Using Sonar
The Sixth International Symposium on Experimental Robotics VI
Simultaneous stochastic mapping and localization
Simultaneous stochastic mapping and localization
Incorporation of Feature Tracking into Simultaneous Localization and Map Building via Sonar Data
Journal of Intelligent and Robotic Systems
Using covariance intersection for SLAM
Robotics and Autonomous Systems
Journal of Intelligent and Robotic Systems
Reduced state representation in delayed-state SLAM
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Information-based compact pose SLAM
IEEE Transactions on Robotics
FISST-SLAM: Finite Set Statistical Approach to Simultaneous Localization and Mapping
International Journal of Robotics Research
How the Location of the Range Sensor Affects EKF-based Localization
Journal of Intelligent and Robotic Systems
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The solution to the simultaneous localization and map building (SLAM) problem where an autonomous vehicle starts in an unknown location in an unknown environment and then incrementally build a map of landmarks present in this environment while simultaneously using this map to compute absolute vehicle location is now well understood. Although a number of SLAM implementations have appeared in the recent literature, the need to maintain the knowledge of the relative relationships between all the landmark location estimates contained in the map makes SLAM computationally intractable in implementations containing more than a few tens of landmarks. This paper presents the theoretical basis and a practical implementation of a feature selection strategy that significantly reduces the computation requirements for SLAM. The paper shows that it is indeed possible to remove a large percentage of the landmarks from the map without making the map building process statistically inconsistent. Furthermore, it is shown that the computational cost of the SLAM algorithm can be reduced by judicious selection of landmarks to be preserved in the map.