Abdelhamid Dine

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The graph-based SLAM (Simultaneous Localization and Mapping) method uses a graph to represent and solve the SLAM problem. The SLAM allows building a map of an unknown environment and simultaneously localizing the robot on this map. This paper presents a temporal analysis of the 3D graph-based SLAM method. We also propose an efficient implementation, on an(More)
This article deals with the computational complexity issue of graphbased simultaneous localization and mapping (SLAM). SLAM allows a robot that is navigating in an unknown environment to build a map of this environment while simultaneously determining the robot pose on this map. Graph-based SLAM is a smoothing method that uses a graph to represent and solve(More)
Simultaneous Localization and Mapping (SLAM) is the process that allows for a robot moving in unknown environment to build the map of the environment while simultaneously use this map to localize itself. Many approaches exist to solve this problem. Graph-based SLAM methods formulate the SLAM problem as a graph where the nodes represent robot and landmarks(More)
An autonomous robot has to localize itself in an unknown area. Simultaneous Localization and Mapping (SLAM) allows for a robot to build a map of an unknown environment and localize simultaneously itself on this map. Graph-based SLAM methods use a graph to represent and solve the SLAM problem. This paper presents an optimized implementation of the(More)
SLAM algorithms are widely used by autonomous robots operating in unknown environments. Several works have presented optimizations mainly focused on the algorithm complexity. New computing technologies (SIMD coprocessors, multicore architecture) can greatly accelerate the processing time but require rethinking the algorithm implementation. This paper(More)
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