Abstract
The existing algorithms are mainly based on the laser or visual inertial odometer, which has problems such as large particle recommendation distribution error, particle consumption, and long running time of the algorithm. An SLAM scheme based on multisensor fusion is proposed, which integrates multisource sensor data including lidar, camera, and IMU and constructs a set of real-time mapping and positioning system with better robustness and higher accuracy. The experimental results show that the proposed SLAM method uses less running time, and the accuracy of map construction is better.
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