Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
Fastslam: a factored solution to the simultaneous localization and mapping problem with unknown data association
A Discussion of Simultaneous Localization and Mapping
Autonomous Robots
A Modified Particle Filter for Simultaneous Localization and Mapping
Journal of Intelligent and Robotic Systems
Fast and accurate SLAM with Rao-Blackwellized particle filters
Robotics and Autonomous Systems
Subjective local maps for hybrid metric-topological SLAM
Robotics and Autonomous Systems
Differential evolution solution to the SLAM problem
Robotics and Autonomous Systems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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This paper presents a novel method for integrating swarm intelligence and line-based representation of environment to solve the simultaneous localization and mapping (SLAM) problem of mobile robots. SLAM is a well-studied problem in mobile robotics. Because of stochastic nature of search strategy in swarm intelligence algorithms, they are very successful compared with other techniques in encountering SLAM problem. Line segment based representation of 2D maps is known to have advantages over raw point data or grid based representation gained from laser range scans. It contains higher geometric information that is closer to human insight and conceptual mapping, which is necessary for robust post processing. It also significantly reduces the memory and time complexity. Mobile robot reads raw laser sensor data in each step of its trajectory and converts it to a set of lines which is used to produce the last sensed map. At the next phase, the algorithm utilizes particle swarm optimization (PSO) and introduces a new evaluation function to find the actual state of the last sensed map inside a global map, which is merged into a global map by introducing a new merge method to reconstruct the global map. We use PSO's ability to run away from local extrema and converge towards an optimum point (i.e. best robot status in the map) by utilizing adaptive inertia weight strategy. We also introduce a new criterion to measure the similarity between the line pairs in the map. The experimental results on real datasets and virtual environments exhibit the algorithm's robustness, accuracy and superior performance on problems that are under consideration in SLAM such as loop closing, correspondence problem, curvature of the walls, and sensor uncertainty.