Teams participating in autonomous robot competitions often measure the environment of the course in advance using a laser range finder, create an environmental map, and realize localization based on this map. However, the method using environmental maps has the problems of difficulty in dealing with environmental changes and complex environments, as well as the high time cost of measuring the environment in advance. To solve these problems, we proposed a localization method that does not require prior maps using RTK-GNSS and LiDAR inertial odometry based on the VGICP, the point cloud registration algorithm. In order to guarantee the accuracy of GNSS location information, we defined three indicators of satellite reception status and proposed a method to discriminate the GNSS environment by setting a threshold value for each indicator. We implemented the proposed methods in an autonomous robot and conducted positioning and autonomous driving experiments on three courses in the university campus with different GNSS reception environments, where the effectiveness of the proposed localization method and GNSS reception environment discrimination method was confirmed.

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