Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Relative position sensing by fusing monocular vision and inertial rate sensors
Relative position sensing by fusing monocular vision and inertial rate sensors
Simultaneous localization and odometry self calibration for mobile robot
Autonomous Robots
Robotics and Autonomous Systems
Estimating the absolute position of a mobile robot using position probability grids
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Calibration for mobile robots with an invariant Jacobian
Robotics and Autonomous Systems
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Localization is a fundamental operation for the navigation of mobile robots. The standard localization algorithms fuse external measurements of the environment with the odometric evolution of the robot pose to obtain its optimal estimation. In this work, we present a different approach to determine the pose using angular measurements discontinuously obtained in time. The presented method is based on an Extended Kalman Filter (EKF) with a state-vector composed of the external angular measurements. This algorithm keeps track of the angles between actual measurements from robot odometric information. This continuous angular estimation allows the consistent use of the triangulation methods to determine the robot pose at any time during its motion. The article reports experimental results that show the localization accuracy obtained by means of the presented approach. These results are compared to the ones obtained applying the EKF algorithm with the standard pose state-vector. For the experiments, an omnidirectional robotic platform with omnidirectional wheels is used.