Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Learning probabilistic motion models for mobile robots
ICML '04 Proceedings of the twenty-first international conference on Machine learning
DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
IEEE Transactions on Robotics
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In this paper, we present a novel data structure representing the environment with occupancy grid cells while each grid map is associated with a set of line features extracted from laser scan points. Due to the fact that line segments are principal elements of artificial environments, they provide considerable geometric information about the environment which can be used for enhancing the accuracy of localization. Orthogonal characteristic of line features is the key issue to guarantee the consistency of the SLAM algorithm by allowing us to deal with lines that are parallel or perpendicular to each other. This behavior allows us to sample robot poses more correctly. As a result, the proposed algorithm can close bigger loops with the same number of particles. Experimental results are carried out using SICK LMS-100 laser scanner which has a maximum range of 20m and Pioneer 3DX mobile robot mapping an indoor environment with the size of 40m × 47m.