The paper proposes an algorithm based on the Multi-State Constraint Kalman Filter (MSCKF) algorithm to construct the map for\nrobots special in the poor GPS signal environment. We can calculate the position of the robots with the data collected by inertial\nmeasurement unit and the features extracted by the camera with MSCKF algorithm in a tight couple way. The paper focuses on the\nway of optimizing the position because we adopt it to compute Kalman gain for updating the state of robots. In order to reduce the\nprocessing time, we design a novel fast Gaussâ??Newton MSCKF algorithm to complete the nonlinear optimization. Compared with\nthe performance of conventional MSCKF algorithm, the novel fast-location algorithm can reduce the processing time with the\nkitti datasets.
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