Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
SLAM for Indoor Environment Using Stereo Vision
GCIS '10 Proceedings of the 2010 Second WRI Global Congress on Intelligent Systems - Volume 02
Rao-blackwellised particle filtering for dynamic Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Inverse Depth Parametrization for Monocular SLAM
IEEE Transactions on Robotics
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In this paper, we propose a system for wheeled robot SLAM and navigation in indoor environments. An omni-directional camera and a laser range finder are the sensors to extract the point features and the line features as the landmarks. In SLAM and self-localization while navigation, we use extended Kalman filter (EKF) to deal with the uncertainty of robot pose and landmark feature estimation. After the map is built, robot can navigate in the environment based on it. We apply two scale path-planning for navigation. The large-scale planning finds an appropriate path from starting point to destination. The local-scale path-planning fills up the drawbacks of the prior step, such as dealing with the static and dynamic obstacles and smoothing the path for easier robot following. Through the experiment results, we show that the proposed system can smoothly and correctly locate itself, build the environment map and navigate in indoor environments.