Research On Autonomous Multi Machine Collaborative SLAM Algorithm Based On Multi Sensor Fusion

With the rapid development of drone technology,drones have been widely used in military,civilian,and scientific research fields.In the field of drone applications,the Simultaneous Localization and Mapping(SLAM)algorithm is an important research direction.Traditional single-drone SLAM algorithms have problems such as low accuracy and poor robustness.Therefore,this paper proposes a multi-sensor fusion-based autonomous multi-drone collaborative SLAM algorithm to improve the collaboration,robustness,and positioning accuracy of the SLAM algorithm.The algorithm is designed for multiple drones to work together and perform multi-source data collection and processing.Each drone is equipped with multiple sensors,including cameras,LIDAR,Inertial Measurement Units(IMUs),etc.,to achieve multi-angle perception and modeling of the environment through the fusion of different sensor data.Drones can communicate and share data with each other,thus collaborating to complete SLAM tasks.In the data fusion process,a factor-weight-based data fusion algorithm is used to improve the accuracy and robustness of the SLAM system by dynamically allocating weights to sensor data.Specifically,the algorithm calculates the weight coefficients for each sensor based on its performance and data quality,and then fuses the data according to the weight coefficients to obtain a more accurate environmental model.In addition,this paper proposes a spatial exploration method based on Frontier Information Structure(FIS),which enables drone swarms to quickly complete exploration and modeling in3 D space.In this method,the effective global coverage path of the drone swarm is first found,and then the local viewpoint set is adjusted using soft constraint optimization.The viewpoint orientation and posture on the single drone path are derived in order,and finally,the position,velocity,and attitude information are combined to estimate the shortest time trajectory of the drone.To verify the effectiveness of this method,a series of simulation and actual flight experiments were conducted.The experimental results show that this method can achieve simultaneous autonomous exploration and SLAM of multiple drones,improving the flight safety and task efficiency of drone swarms.In addition,due to the integration of obstacle avoidance algorithms,this method has strong practicality and applicability and can be applied to autonomous exploration and SLAM tasks of drone swarms in various complex environments.In conclusion,this paper proposes a multi-sensor fusion-based autonomous multi-drone collaborative SLAM algorithm,which combines data from multiple drones and multiple sensors to achieve simultaneous positioning,mapping,and collaborative path planning for multiple drones.The algorithm can improve the flight safety and task efficiency of drone swarms and has strong practicality and applicability.This research provides an effective solution for the efficient collaborative exploration and environmental modeling tasks of drones in complex environments,with important theoretical and practical significance.